Maíra R. Rodrigues

Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations

Significance The EPIGEN Brazil Project is the largest Latin-American initiative to study the genomic diversity of admixed populations and its effect on phenotypes. We studied 6,487 Brazilians from three population-based cohorts with different geographic and demographic backgrounds. We identified ancestry components of these populations at a previously unmatched geographic resolution. We broadened our understanding of the African diaspora, the principal destination of which was Brazil, by revealing an African ancestry component that likely derives from the slave trade from Bantu/eastern African populations. In the context of the current debate about how the pattern of deleterious mutations varies between Africans and Europeans, we use whole-genome data to show that continental admixture is the main and complex determinant of the amount of deleterious genotypes in admixed individuals. While South Americans are underrepresented in human genomic diversity studies, Brazil has been a classical model for population genetics studies on admixture. We present the results of the EPIGEN Brazil Initiative, the most comprehensive up-to-date genomic analysis of any Latin-American population. A population-based genome-wide analysis of 6,487 individuals was performed in the context of worldwide genomic diversity to elucidate how ancestry, kinship, and inbreeding interact in three populations with different histories from the Northeast (African ancestry: 50%), Southeast, and South (both with European ancestry >70%) of Brazil. We showed that ancestry-positive assortative mating permeated Brazilian history. We traced European ancestry in the Southeast/South to a wider European/Middle Eastern region with respect to the Northeast, where ancestry seems restricted to Iberia. By developing an approximate Bayesian computation framework, we infer more recent European immigration to the Southeast/South than to the Northeast. Also, the observed low Native-American ancestry (6–8%) was mostly introduced in different regions of Brazil soon after the European Conquest. We broadened our understanding of the African diaspora, the major destination of which was Brazil, by revealing that Brazilians display two within-Africa ancestry components: one associated with non-Bantu/western Africans (more evident in the Northeast and African Americans) and one associated with Bantu/eastern Africans (more present in the Southeast/South). Furthermore, the whole-genome analysis of 30 individuals (42-fold deep coverage) shows that continental admixture rather than local post-Columbian history is the main and complex determinant of the individual amount of deleterious genotypes.

Phred-Phrap package to analyses tools: a pipeline to facilitate population genetics re-sequencing studies

BackgroundTargeted re-sequencing is one of the most powerful and widely used strategies for population genetics studies because it allows an unbiased screening for variation that is suitable for a wide variety of organisms. Examples of studies that require re-sequencing data are evolutionary inferences, epidemiological studies designed to capture rare polymorphisms responsible for complex traits and screenings for mutations in families and small populations with high incidences of specific genetic diseases. Despite the advent of next-generation sequencing technologies, Sanger sequencing is still the most popular approach in population genetics studies because of the widespread availability of automatic sequencers based on capillary electrophoresis and because it is still less prone to sequencing errors, which is critical in population genetics studies. Two popular software applications for re-sequencing studies are Phred-Phrap-Consed-Polyphred, which performs base calling, alignment, graphical edition and genotype calling and DNAsp, which performs a set of population genetics analyses. These independent tools are the start and end points of basic analyses. In between the use of these tools, there is a set of basic but error-prone tasks to be performed with re-sequencing data.ResultsIn order to assist with these intermediate tasks, we developed a pipeline that facilitates data handling typical of re-sequencing studies. Our pipeline: (1) consolidates different outputs produced by distinct Phred-Phrap-Consed contigs sharing a reference sequence; (2) checks for genotyping inconsistencies; (3) reformats genotyping data produced by Polyphred into a matrix of genotypes with individuals as rows and segregating sites as columns; (4) prepares input files for haplotype inferences using the popular software PHASE; and (5) handles PHASE output files that contain only polymorphic sites to reconstruct the inferred haplotypes including polymorphic and monomorphic sites as required by population genetics software for re-sequencing data such as DNAsp.ConclusionWe tested the pipeline in re-sequencing studies of haploid and diploid data in humans, plants, animals and microorganisms and observed that it allowed a substantial decrease in the time required for sequencing analyses, as well as being a more controlled process that eliminates several classes of error that may occur when handling datasets. The pipeline is also useful for investigators using other tools for sequencing and population genetics analyses.

Socioeconomic and Nutritional Factors Account for the Association of Gastric Cancer with Amerindian Ancestry in a Latin American Admixed Population

Gastric cancer is one of the most lethal types of cancer and its incidence varies worldwide, with the Andean region of South America showing high incidence rates. We evaluated the genetic structure of the population from Lima (Peru) and performed a case-control genetic association study to test the contribution of African, European, or Native American ancestry to risk for gastric cancer, controlling for the effect of non-genetic factors. A wide set of socioeconomic, dietary, and clinic information was collected for each participant in the study and ancestry was estimated based on 103 ancestry informative markers. Although the urban population from Lima is usually considered as mestizo (i.e., admixed from Africans, Europeans, and Native Americans), we observed a high fraction of Native American ancestry (78.4% for the cases and 74.6% for the controls) and a very low African ancestry (<5%). We determined that higher Native American individual ancestry is associated with gastric cancer, but socioeconomic factors associated both with gastric cancer and Native American ethnicity account for this association. Therefore, the high incidence of gastric cancer in Peru does not seem to be related to susceptibility alleles common in this population. Instead, our result suggests a predominant role for ethnic-associated socioeconomic factors and disparities in access to health services. Since Native Americans are a neglected group in genomic studies, we suggest that the population from Lima and other large cities from Western South America with high Native American ancestry background may be convenient targets for epidemiological studies focused on this ethnic group.

Cooperative Interactions: An Exchange Values Model

In non-economic cooperative applications with resource constraints, explicitly motivating cooperation is important so that autonomous service providers have incentives to cooperate. When participants of such applications have different skills and expectations over services, it may be that an agent receives less than expected from a cooperation. A decision-making strategy over interactions in this context must consider not only the motivation to cooperate, but also which interactions to perform to cope with resource limitations. In this paper, we present a computational approach for modelling non-economic cooperative interactions based on the theory of exchange values. Here, exchange values are used to motivate cooperative interactions, and to allow agents to identify successful and unsuccessful cooperations with others, in order to limit service provision and to improve the number of successful interactions. We also present a scenario in which agents participate in a cooperative application in the bioinformatics domain, and show how agents can improve their interactions using the proposed approach.

A minimum set of ancestry informative markers for determining admixture proportions in a mixed American population: the Brazilian set

The Brazilian population is considered to be highly admixed. The main contributing ancestral populations were European and African, with Amerindians contributing to a lesser extent. The aims of this study were to provide a resource for determining and quantifying individual continental ancestry using the smallest number of SNPs possible, thus allowing for a cost- and time-efficient strategy for genomic ancestry determination. We identified and validated a minimum set of 192 ancestry informative markers (AIMs) for the genetic ancestry determination of Brazilian populations. These markers were selected on the basis of their distribution throughout the human genome, and their capacity of being genotyped on widely available commercial platforms. We analyzed genotyping data from 6487 individuals belonging to three Brazilian cohorts. Estimates of individual admixture using this 192 AIM panels were highly correlated with estimates using ~370 000 genome-wide SNPs: 91%, 92%, and 74% of, respectively, African, European, and Native American ancestry components. Besides that, 192 AIMs are well distributed among populations from these ancestral continents, allowing greater freedom in future studies with this panel regarding the choice of reference populations. We also observed that genetic ancestry inferred by AIMs provides similar association results to the one obtained using ancestry inferred by genomic data (370 K SNPs) in a simple regression model with rs1426654, related to skin pigmentation, genotypes as dependent variable. In conclusion, these markers can be used to identify and accurately quantify ancestry of Latin Americans or US Hispanics/Latino individuals, in particular in the context of fine-mapping strategies that require the quantification of continental ancestry in thousands of individuals.

Genomic ancestry, Self-rated health and its association with mortality in an admixed population: 10 year follow-up of the Bambui-Epigen (Brazil) cohort study of ageing

Background Self-rated health (SRH) has strong predictive value for mortality in different contexts and cultures, but there is inconsistent evidence on ethnoracial disparities in SRH in Latin America, possibly due to the complexity surrounding ethnoracial self-classification. Materials/Methods We used 370,539 Single Nucleotide Polymorphisms (SNPs) to examine the association between individual genomic proportions of African, European and Native American ancestry, and ethnoracial self-classification, with baseline and 10-year SRH trajectories in 1,311 community dwelling older Brazilians. We also examined whether genomic ancestry and ethnoracial self-classification affect the predictive value of SRH for subsequent mortality. Results European ancestry predominated among participants, followed by African and Native American (median = 84.0%, 9.6% and 5.3%, respectively); the prevalence of Non-White (Mixed and Black) was 39.8%. Persons at higher levels of African and Native American genomic ancestry, and those self-identified as Non-White, were more likely to report poor health than other groups, even after controlling for socioeconomic conditions and an array of self-reported and objective physical health measures. Increased risks for mortality associated with worse SRH trajectories were strong and remarkably similar (hazard ratio ~3) across all genomic ancestry and ethno-racial groups. Conclusions Our results demonstrated for the first time that higher levels of African and Native American genomic ancestry—and the inverse for European ancestry—were strongly correlated with worse SRH in a Latin American admixed population. Both genomic ancestry and ethnoracial self-classification did not modify the strong association between baseline SRH or SRH trajectory, and subsequent mortality.

Bayesian inferences suggest that Amazon Yunga Natives diverged from Andeans less than 5000 ybp: Implications for South American prehistory

BackgroundArchaeology reports millenary cultural contacts between Peruvian Coast-Andes and the Amazon Yunga, a rainforest transitional region between Andes and Lower Amazonia. To clarify the relationships between cultural and biological evolution of these populations, in particular between Amazon Yungas and Andeans, we used DNA-sequence data, a model-based Bayesian approach and several statistical validations to infer a set of demographic parameters.ResultsWe found that the genetic diversity of the Shimaa (an Amazon Yunga population) is a subset of that of Quechuas from Central-Andes. Using the Isolation-with-Migration population genetics model, we inferred that the Shimaa ancestors were a small subgroup that split less than 5300 years ago (after the development of complex societies) from an ancestral Andean population. After the split, the most plausible scenario compatible with our results is that the ancestors of Shimaas moved toward the Peruvian Amazon Yunga and incorporated the culture and language of some of their neighbors, but not a substantial amount of their genes. We validated our results using Approximate Bayesian Computations, posterior predictive tests and the analysis of pseudo-observed datasets.ConclusionsWe presented a case study in which model-based Bayesian approaches, combined with necessary statistical validations, shed light into the prehistoric demographic relationship between Andeans and a population from the Amazon Yunga. Our results offer a testable model for the peopling of this large transitional environmental region between the Andes and the Lower Amazonia. However, studies on larger samples and involving more populations of these regions are necessary to confirm if the predominant Andean biological origin of the Shimaas is the rule, and not the exception.

DIVERGENOME: a bioinformatics platform to assist population genetics and genetic epidemiology studies.

Large‐scale genomics initiatives such as the HapMap project and the 1000‐genomes rely on powerful bioinformatics support to assist data production and analysis. Contrastingly, few bioinformatics platforms oriented to smaller research groups exist to store, handle, share, and integrate data from different sources, as well as to assist these scientists to perform their analyses efficiently. We developed such a bioinformatics platform, DIVERGENOME, to assist population genetics and genetic epidemiology studies performed by small‐ to medium‐sized research groups. The platform is composed of two integrated components, a relational database (DIVERGENOMEdb), and a set of tools to convert data formats as required by popular software in population genetics and genetic epidemiology (DIVERGENOMEtools). In DIVERGENOMEdb, information on genotypes, polymorphism, laboratory protocols, individuals, populations, and phenotypes is organized in projects. These can be queried according to permissions. Here, we validated DIVERGENOME through a use case regarding the analysis of SLC2A4 genetic diversity in human populations. DIVERGENOME, with its intuitive Web interface and automatic data loading capability, facilitates its use by individuals without bioinformatics background, allowing complex queries to be easily interrogated and straightforward data format conversions (not available in similar platforms). DIVERGENOME is open source, freely available, and can be accessed online (pggenetica.icb.ufmg.br/divergenome) or hosted locally. Genet. Epidemiol. 36:360–367, 2012.

PRODIS: a proteomics data management system with support to experiment tracking

BackgroundA research area that has greatly benefited from the development of new and improved analysis technologies is Proteomics and large amounts of data have been generated by proteomic analysis as a consequence. Previously, the storage, management and analysis of these data have been done manually. This is, however, incompatible with the volume of data generated by modern proteomic analysis. Several attempts have been made to automate the tasks of data analysis and management. In this work we propose PRODIS (Proteomics Database Integrated System), a system for proteomic experimental data management. The proposed system enables an efficient management of the proteomic experimentation workflow, simplifies controlling experiments and associated data and establishes links between similar experiments through the experiment tracking function.ResultsPRODIS is fully web based which simplifies data upload and gives the system the flexibility necessary for use in complex projects. Data from Liquid Chromatography, 2D-PAGE and Mass Spectrometry experiments can be stored in the system. Moreover, it is simple to use, researchers can insert experimental data directly as experiments are performed, without the need to configure the system or change their experiment routine. PRODIS has a number of important features, including a password protected system in which each screen for data upload and retrieval is validated; users have different levels of clearance, which allow the execution of tasks according to the user clearance level. The system allows the upload, parsing of files, storage and display of experiment results and images in the main formats used in proteomics laboratories: for chromatographies the chromatograms and lists of peaks resulting from separation are stored; For 2D-PAGE images of gels and the files resulting from the analysis are stored, containing information on positions of spots as well as its values of intensity, volume, etc; For Mass Spectrometry, PRODIS presents a function for completion of the mapping plate that allows the user to correlate the positions in plates to the samples separated by 2D-PAGE. Furthermore PRODIS allows the tracking of experiments from the first stage until the final step of identification, enabling an efficient management of the complete experimental process.ConclusionsThe construction of data management systems for Proteomics data importing and storing is a relevant subject. PRODIS is a system complementary to other proteomics tools that combines a powerful storage engine (the relational database) and a friendly access interface, aiming to assist Proteomics research directly at data handling and storage.

Evaluating dynamic services in bioinformatics

In dynamic applications characterised by a variety of alternative services with the same functionality but heterogeneous results, agents requesting services must find an efficient way to select a service provider from alternatives. In this context, this paper proposes an evaluation method to analyse the outcome of dynamic service, in order to provide a guide for agents in future decision-making over alternative interaction partners. We consider the application of the evaluation method to the bioinformatics domain and present empirical results that support the need for dynamic evaluation of services in that domain.