Mexico harbors great cultural and ethnic diversity, yet fine-scale patterns of human genome-wide variation from this region remain largely uncharacterized. We studied genomic variation within Mexico from over 1,000 individuals representing 20 indigenous and 11 mestizo populations. We found striking genetic stratification among indigenous populations within Mexico at varying degrees of geographic isolation. Some groups were as differentiated as Europeans are from East Asians. Pre-Columbian genetic substructure is recapitulated in the indigenous ancestry of admixed mestizo individuals across the country. Furthermore, two independently phenotyped cohorts of Mexicans and Mexican Americans showed a significant association between sub-continental ancestry and lung function. Thus, accounting for fine-scale ancestry patterns is critical for medical and population genetic studies within Mexico, in Mexican-descent populations, and likely in many other populations worldwide.

No abstract

Due to the conditions in which traditional sheep production systems operate, the evaluation of animal growth from live weight (LW) is limited by the high cost of the livestock scale as well as the sophisticated maintenance required. In this scenario, in recent years, biometric measurements have been investigated as an accurate indirect method to predict the LW of farm animals. Therefore, the present study was undertaken to examine different models for predicting the body weight of growing lambs using the body volume (BV) formula. Body volume, heart girth (HG) and body length (BL) data of 290 lambs aged between two and eight months were recorded. Body volume was calculated from HG and BL data using a formula that calculates the volume of a cylinder. The estimation of LW from the BV formula was achieved through regression equations using three mathematical models (linear, quadratic and exponential). The mean values of LW, HG, BL and BV of the lambs were 29.12±12.04kg, 70.00±11.69cm, 38.40±6.43cm and 23.93±9.90dm3, respectively. The correlation coefficient between LW and BV was r = 0.96 (P<0.001). The quadratic model showed the highest coefficient of determination (0.93) and the lowest prediction error (3.29kg). Under the experimental conditions adopted in this study, it is possible to predict the live weight of growing lambs using the body volume formula.

IntroductionModern high-throughput genomic technologies have allowed the large-scale characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Computational tools and mathematical algorithms have been created aiming to integrate, organize and mine the wealth of data generated. Technologies for the detection of different types of genomic alterations have been developed and applied to the analyses of living organisms and, in particular, cancer genomes. It is clear that studies based on a single technology are limited compared with the extent of knowledge that can be acquired using different technological platforms together. Hence, there is a need for systematic methodologies facilitating data management, visualization and integration. Such methodologies should aim to permit a proper analysis of the biological implications of findings, without sacrificing computational efficiency or mathematical and statistical rigour. With these purposes in mind, we have designed and implemented a data driven 3-state model for multidimensional data integration (3-MDI). ResultsLevel 1 and Level 2 data sets were pre-processed, and genes were selected in each platform based on the summary statistics presented in Table 1. Selected targets for mRNA expression and methylation were coded with our 3-state model {1,0, −1}, with 1 for upregulated or hypermethylation, 0 for no change, and −1 for downregulated or hypomethylation.The Level 3 somatic mutation data set was re-ordered by genes and coded in a 2-state format {0,1} rather than a {1,0, −1} 3-state format in order to avoid an ad hoc threshold for the classification of hypo/hyper mutation. The 2-state format represents the presence (1) or absence (0) of a mutation in a gene.Using these 3 platforms we could find up to 18 possible scenarios for further analysis (Fig. 1). Note that a single platform reports larger gene numbers as changing (e.g., pure gene expression cases {0,0, −1} with 1274 genes and {0,0,1} with 1418 genes), and numbers of genes changing are lower when combining multiple platforms and requiring congruent behavior, which is somehow exacerbated by to the fact that not all samples have measurements in all platforms.Background: Genomic technologies have allowed a large-scale molecular characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Advanced platforms for the detection of different types of genomic alterations have been developed and applied to analyses of living organisms and, in particular, cancer genomes. It is clear now that studies based on a single platform are limited compared with the extent of knowledge gain possible when exploiting different platforms together. There is therefore a need for systematic methodologies facilitating data management, visualization, and integration.Materials and Methods: We present a 3-state model (3-MDI) that integrates several technological platforms, visualizing and prioritizing different biological scenarios, ...

No abstract