Implications of the Genographic Project for Molecular Anthropologists

2009 ◽  
Vol 16 (2) ◽  
pp. 193-194 ◽  
Author(s):  
Ripan Malhi
Keyword(s):  
Native American ◽  
Important Implication ◽  
Field Component ◽  
Genetic Ancestry ◽  
Data Sets ◽  
Molecular Anthropology ◽  
Alternative Approach ◽  
The Future ◽  
Existing Data

Many molecular anthropologists no longer incorporate a field component into their research; rather, they rely on analyzing existing data sets and/or on collaborating with field researchers to obtain samples for analysis. This trend in molecular anthropology, combined with the aggressive agenda of the Genographic Project, has an important implication for the future of the discipline. If Native American communities are exposed to genetic ancestry research largely through the Genographic Project, they are less likely to see that there are multiple ways for Native American communities to interact with genetic researchers. Molecular anthropologists are in a position to offer an alternative approach to research by pursuing enduring and mutually beneficial collaborative projects with Native American communities.

An Introduction to Finance and Investment

2018 ◽  
Author(s):  
Colin Mayer ◽  
Stefano Micossi ◽  
Marco Onado ◽  
Marco Pagano ◽  
Andrea Polo
Keyword(s):  
Sovereign Debt ◽  
Large Body ◽  
Financial System ◽  
Policy Formulation ◽  
Data Sets ◽  
Body Of Knowledge ◽  
The Future ◽  
Debt Crises ◽  
European Economies ◽  
Existing Data

This chapter reviews the problems of finance and investment confronting European economies and summarizes the approaches that can be adopted to address them. The chapters in this volume provide one of the most comprehensive and thorough analyses of any financial system that has been undertaken to date. They reflect a large body of research using new and existing data sets, employing advanced empirical tools, and exploiting the unique insights provided by the tumultuous events of the financial and sovereign debt crises. Together they therefore comprise an exceptional body of knowledge to guide policy formulation in the future.

scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Sets ◽  
Data Handling ◽  
Online Database ◽  
Genetic Studies ◽  
Wide Range ◽  
Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

scholarly journals Genetic ancestry inferred from autosomal and Y chromosome markers and HLA genotypes in Type 1 Diabetes from an admixed Brazilian population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Santiago de Sousa Azulay ◽  
Luís Cristóvão Porto ◽  
Dayse Aparecida Silva ◽  
Maria da Glória Tavares ◽  
Roberta Maria Duailibe Ferreira Reis ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Native American ◽  
Y Chromosome ◽  
Chromosome Analysis ◽  
Brazilian Population ◽  
Genetic Ancestry ◽  
Sample Sizes ◽  
Chromosome Markers ◽  
Hla Genotypes

AbstractThis study aimed to investigate the relationship between genetic ancestry inferred from autosomal and Y chromosome markers and HLA genotypes in patients with Type 1 Diabetes from an admixed Brazilian population. Inference of autosomal ancestry; HLA-DRB1, -DQA1 and -DQB1 typifications; and Y chromosome analysis were performed. European autosomal ancestry was about 50%, followed by approximately 25% of African and Native American. The European Y chromosome was predominant. The HLA-DRB1*03 and DRB1*04 alleles presented risk association with T1D. When the Y chromosome was European, DRB1*03 and DRB1*04 homozygote and DRB1*03/DRB1*04 heterozygote genotypes were the most frequent. The results suggest that individuals from Maranhão have a European origin as their major component; and are patrilineal with greater frequency from the R1b haplogroup. The predominance of the HLA-DRB1*03 and DRB1*04 alleles conferring greater risk in our population and being more frequently related to the ancestry of the European Y chromosome suggests that in our population, the risk of T1D can be transmitted by European ancestors of our process miscegenation. However, the Y sample sizes of Africans and Native Americans were small, and further research should be conducted with large mixed sample sizes to clarify this possible association.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

Dehydration of seabird prey during transport to the colony: effects on wet weight energy densities

1987 ◽  
Vol 65 (11) ◽  
pp. 2822-2824 ◽  
Author(s):  
W. A. Montevecchi ◽  
J. F. Piatt
Keyword(s):  
Water Content ◽  
Dry Weight ◽  
Data Sets ◽  
Fresh Weight ◽  
Present Evidence ◽  
Wet Weight ◽  
Cautious Interpretation ◽  
Existing Data

We present evidence to indicate that dehydration of prey transported by seabirds from capture sites at sea to chicks at colonies inflates estimates of wet weight energy densities. These findings and a comparison of wet and dry weight energy densities reported in the literature emphasize the importance of (i) accurate measurement of the fresh weight and water content of prey, (ii) use of dry weight energy densities in comparisons among species, seasons, and regions, and (iii) cautious interpretation and extrapolation of existing data sets.

scholarly journals Meta-Analysis Combining New and Existing Data Sets Confirms that the TERT–CLPTM1L Locus Influences Melanoma Risk

2012 ◽  
Vol 132 (2) ◽  
pp. 485-487 ◽  
Author(s):  
Matthew H. Law ◽  
Grant W. Montgomery ◽  
Kevin M. Brown ◽  
Nicholas G. Martin ◽  
Graham J. Mann ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Data Sets ◽  
Melanoma Risk ◽  
Existing Data

scholarly journals Revealing the Mutational Spectrum in Southern Africans With Amyotrophic Lateral Sclerosis

2022 ◽  
Vol 8 (1) ◽  
pp. e654
Author(s):  
Melissa Nel ◽  
Amokelani C. Mahungu ◽  
Nomakhosazana Monnakgotla ◽  
Gerrit R. Botha ◽  
Nicola J. Mulder ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Population Group ◽  
Genetic Ancestry ◽  
Familial Case ◽  
Data Sets ◽  
Variant Of Uncertain Significance ◽  
Pathogenic Variants ◽  
Sporadic Als ◽  
Uncertain Significance ◽  
Lateral Sclerosis

Background and ObjectivesTo perform the first screen of 44 amyotrophic lateral sclerosis (ALS) genes in a cohort of African genetic ancestry individuals with ALS using whole-genome sequencing (WGS) data.MethodsOne hundred three consecutive cases with probable/definite ALS (using the revised El Escorial criteria), and self-categorized as African genetic ancestry, underwent WGS using various Illumina platforms. As population controls, 238 samples from various African WGS data sets were included. Our analysis was restricted to 44 ALS genes, which were curated for rare sequence variants and classified according to the American College of Medical Genetics guidelines as likely benign, uncertain significance, likely pathogenic, or pathogenic variants.ResultsThirteen percent of 103 ALS cases harbored pathogenic variants; 5 different SOD1 variants (N87S, G94D, I114T, L145S, and L145F) in 5 individuals (5%, 1 familial case), pathogenic C9orf72 repeat expansions in 7 individuals (7%, 1 familial case) and a likely pathogenic ANXA11 (G38R) variant in 1 individual. Thirty individuals (29%) harbored ≥1 variant of uncertain significance; 10 of these variants had limited pathogenic evidence, although this was insufficient to permit confident classification as pathogenic.DiscussionOur findings show that known ALS genes can be expected to identify a genetic cause of disease in >11% of sporadic ALS cases of African genetic ancestry. Similar to European cohorts, the 2 most frequent genes harboring pathogenic variants in this population group are C9orf72 and SOD1.

scholarly journals Prediction of Eye, Hair and Skin Color in Admixed Populations of Latin America

2020 ◽  
Author(s):  
Sagnik Palmal ◽  
Kaustubh Adhikari ◽  
Javier Mendoza-Revilla ◽  
Macarena Fuentes-Guajardo ◽  
Caio C. Silva de Cerqueira ◽  
...  
Keyword(s):  
Native American ◽  
Skin Color ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Skin Pigmentation ◽  
Genetic Ancestry ◽  
Learning Approaches ◽  
Latin Americans ◽  
Eye Color ◽  
The Impact

AbstractWe report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

scholarly journals Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam

2019 ◽  
Vol 8 (10) ◽  
pp. 3797-3802
Keyword(s):  
Trend Analysis ◽  
Error Estimates ◽  
Data Sets ◽  
Forecasting Models ◽  
Level Data ◽  
Forecasting Method ◽  
The Future ◽  
Current Scenario ◽  
Stochastic Forecasting

This article forecasts the future values using stochastic forecasting models for specified fitted values by using downscaling data, which are collected from Sathanoor Dam gauging site. Due to the demand of the water in this current scenario, this study analyzed the perdays Discharge level data collected from Sathanoor Dam where the outcome is predicted in a downscaling data sets in hydrology, extended Thomas –Fiering, ARIMA, MLE models, is used to estimate perdays discharge level data of each month. The error estimates RMSE, MAE of forecasts from above models is compared to identify the most suitable approaches for forecasting trend analysis.

Prior Beliefs and Ambiguity Attitudes in Decision from Experience

2021 ◽  
Author(s):  
Ilke Aydogan
Keyword(s):  
Decision Analysis ◽  
Crucial Role ◽  
Decision Model ◽  
Data Sets ◽  
Subjective Probabilities ◽  
Prior Beliefs ◽  
Decisions From Experience ◽  
Ambiguity Attitudes ◽  
Existing Data

Prior beliefs and their updating play a crucial role in decisions under uncertainty, and theories about them have been well established in classical Bayesianism. Yet, they are almost absent for ambiguous decisions from experience. This paper proposes a new decision model that incorporates the role of prior beliefs, beyond the role of ambiguity attitudes, into the analysis of such decisions. Hence, it connects ambiguity theories, popular in economics, with decision from experience, popular (mostly) in psychology, to the benefit of both. A reanalysis of some existing data sets from the literature on decisions from experience shows that the model that incorporates prior beliefs into the estimation of subjective probabilities outperforms the commonly used model that approximates subjective probabilities with observed relative frequencies. Controlling for subjective priors, we obtain more accurate measurements of ambiguity attitudes, and thus a new explanation of the gap between decision from description and decision from experience. This paper was accepted by Manel Baucells, decision analysis.

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