Guido Mazzotti

Bio: Guido Mazzotti is an academic researcher from Cayetano Heredia University. The author has contributed to research in topics: Mental health & Latin Americans. The author has an hindex of 11, co-authored 18 publications receiving 1604 citations.

Genetic Variation and Population Structure in Native Americans

Abstract: We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians—signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.

Geographic Patterns of Genome Admixture in Latin American Mestizos

Abstract: The large and diverse population of Latin America is potentially a powerful resource for elucidating the genetic basis of complex traits through admixture mapping. However, no genome-wide characterization of admixture across Latin America has yet been attempted. Here, we report an analysis of admixture in thirteen Mestizo populations (i.e. in regions of mainly European and Native settlement) from seven countries in Latin America based on data for 678 autosomal and 29 X-chromosome microsatellites. We found extensive variation in Native American and European ancestry (and generally low levels of African ancestry) among populations and individuals, and evidence that admixture across Latin America has often involved predominantly European men and both Native and African women. An admixture analysis allowing for Native American population subdivision revealed a differentiation of the Native American ancestry amongst Mestizos. This observation is consistent with the genetic structure of pre-Columbian populations and with admixture having involved Natives from the area where the Mestizo examined are located. Our findings agree with available information on the demographic history of Latin America and have a number of implications for the design of association studies in population from the region.

A Genomewide Admixture Map for Latino Populations

Abstract: Admixture mapping is an economical and powerful approach for localizing disease genes in populations of recently mixed ancestry and has proven successful in African Americans. The method holds equal promise for Latinos, who typically inherit a mix of European, Native American, and African ancestry. However, admixture mapping in Latinos has not been practical because of the lack of a map of ancestry-informative markers validated in Native American and other populations. To address this, we screened multiple databases, containing millions of markers, to identify 4,186 markers that were putatively informative for determining the ancestry of chromosomal segments in Latino populations. We experimentally validated each of these markers in at least 232 new Latino, European, Native American, and African samples, and we selected a subset of 1,649 markers to form an admixture map. An advantage of our strategy is that we focused our map on markers distinguishing Native American from other ancestries and restricted it to markers with very similar frequencies in Europeans and Africans, which decreased the number of markers needed and minimized the possibility of false disease associations. We evaluated the effectiveness of our map for localizing disease genes in four Latino populations from both North and South America.

Mental health research priorities in low- and middle-income countries of Africa, Asia, Latin America and the Caribbean.

Abstract: BACKGROUND: Studies suggest a paucity of and lack of prioritisation in mental health research from low- and middle-income (LAMI) countries. AIMS: To investigate research priorities in mental health among researchers and other stakeholders in LAMI countries. METHOD: We used a two-stage design that included identification, through literature searches and snowball technique, of researchers and stakeholders in 114 countries of Africa, Asia, Latin America and the Caribbean; and a mail survey on priorities in research. RESULTS: The study identified broad agreement between researchers and stakeholders and across regions regarding research priorities. Epidemiology (burden and risk factors), health systems and social science ranked highest for type of research. Depression/anxiety, substance use disorders and psychoses; and children and adolescents, women, and people exposed to violence/trauma were prioritised among the disorders and population groups respectively. Important criteria for prioritising research were burden of disease, social justice, and availability of funds. Stakeholder groups differed in the importance they gave to the personal interest of researchers as a criterion for prioritising research. Researchers' and stakeholders' priorities were consistent with burden of disease estimates, however suicide was underprioritised compared with its burden. Researchers' and stakeholders' priorities were also largely congruent with the researchers' projects. CONCLUSIONS: The results of this first ever conducted survey of researchers and stakeholders regarding research priorities in mental health suggest that it should be possible to develop consensus at regional and international levels regarding the research agenda that is necessary to support health system objectives in LAMI countries. Language: en

Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

Abstract: The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.

Clumpak: a program for identifying clustering modes and packaging population structure inferences across K

Abstract: The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present CLUMPAK (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, CLUMPAK identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software CLUMPP. Next, CLUMPAK identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in CLUMPP and simplifying the comparison of clustering results across different K values. CLUMPAK incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. CLUMPAK, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.

Ancient Admixture in Human History

Abstract: Population mixture is an important process in biology. We present a suite of methods for learning about population mixtures, implemented in a software package called ADMIXTOOLS, that support formal tests for whether mixture occurred and make it possible to infer proportions and dates of mixture. We also describe the development of a new single nucleotide polymorphism (SNP) array consisting of 629,433 sites with clearly documented ascertainment that was specifically designed for population genetic analyses and that we genotyped in 934 individuals from 53 diverse populations. To illustrate the methods, we give a number of examples that provide new insights about the history of human admixture. The most striking finding is a clear signal of admixture into northern Europe, with one ancestral population related to present-day Basques and Sardinians and the other related to present-day populations of northeast Asia and the Americas. This likely reflects a history of admixture between Neolithic migrants and the indigenous Mesolithic population of Europe, consistent with recent analyses of ancient bones from Sweden and the sequencing of the genome of the Tyrolean "Iceman."

Grand challenges in global mental health

Abstract: A consortium of researchers, advocates and clinicians announces here research priorities for improving the lives of people with mental illness around the world, and calls for urgent action and investment.

Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data.

Abstract: Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40–270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17–43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3–26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).