[서평]「Algorithms on Strings, Trees, and Sequences」

Admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. The power of this method relies on the ability of ancestry informative markers (AIMs) to infer ancestry along the chromosomes of admixed individuals. In this study, more than one million SNPs from HapMap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: Fisher Information Content (FIC), Shannon Information Content (SIC), F statistics (FST), Informativeness for Assignment Measure (In), and the Absolute Allele Frequency Differences (delta, δ). The objectives are to compare these measures of informativeness to select SNP markers for ancestry inference, and to determine the accuracy of AIM panels selected by each measure in estimating the contributions of the ancestors to the admixed population. FST and In had the highest Spearman correlation and the best agreement as measured by Kappa statistics based on deciles. Although the different measures of marker informativeness performed comparably well, analyses based on the top 1 to 10% ranked informative markers of simulated data showed that In was better in estimating ancestry for an admixed population. Although millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. In this article, we compared various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations and showed that the In measure estimates ancestry proportion (in an admixed population) with lower bias and mean square error.

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Comparison of measures of marker informativeness for ancestry and admixture mapping

There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.

Mapping genes that predict treatment outcome in admixed populations.

In its natural framework, genetic information is embedded within a geographic context. Plants and animals are directly influenced by the specific characteristics of their surrounding environment. Therefore, spatial information is a potentially important element to be considered in trying to understand genetic resources. For many years, Geographical Information Science (GIScience) turned toward environmental modelling, generally to demonstrate how GIS basic features could be efficiently applied to fields related to the natural sciences. However, despite its current predominance in life sciences and its direct application to concerns of public society (health, food), genetics had heretofore remained outside the scope of research by the GIScience community while biologists were developing approaches based on GIS, which conducted to the elaboration of «landscape genetics». The present GIScience approach to linking genetics and geographics obviously places emphasis on geographic information, unlike most studies in this domain. This perspective is developed through an application to two case studies to assess the potential contribution of GIScience to conservation biology. The main one is provided by Econogene, a European research project aiming at promoting the sustainable conservation of genetic resources in sheep and goats. These small ruminants have considerable economic importance in marginal agrosystems in Europe. The surveying of their genetic resources makes it possible to highlight endangered breeds having high distinctiveness and priority for conservation. The second case study is complementary and supplies wild species data to demonstrate how genetic information is used to assess conservation measures applied to the endangered Scandinavian Brown Bear. Advanced molecular technologies make it possible to efficiently measure genetic information. In parallel, considerable advancements in computer science have led to the development of sophisticated GIS software and methods. The joining of molecular biology and GIScience enables novel and complementary methods of tackling some of the challenging issues related to evolutionary processes. This tentative application of diverse facets of GIScience to molecular biology addresses three distinct issues. Firstly, considering the vast quantity of information collected within the Econogene project, exploratory data analysis methods are applied to extract useful information from large spatially explicit genetic data sets. This category of GIS tools facilitates investigations to understand the geographic distribution of genetic diversity among sheep and goat breeds as well as its variation according to environmental parameters. Secondly, a review of population genetics literature having revealed weaknesses about the way spatial genetic data are generally represented on maps, the semiology of graphics is used to produce improved thematic maps representing patterns of genetic diversity. The recourse to high-performance cartography improves the interpretation of data, and facilitates the transmission of the results as well as their communication between researchers, or between researchers and the general public. Thirdly, this combination of GIScience with molecular biology mainly leads to the development of a spatial analysis method to detect signatures of natural selection within the genome. The method is adapted to a precise conception of environmental modelling, advocating the implementation of simple models in order to better grasp the functioning of natural processes, and recognizing an inescapable uncertainty. To find out these signatures, spatial analysis takes advantage of the evolution of genetics toward genomics and of the subsequent availability of large data sets generated by large genome scans. This permits to compute many simultaneous univariate logistic regression models and to identify specific regions of the genome which are selected by environmental parameters. These are identical to the ones detected by a standard approach developed in population genomics.

The geographical dimension of genetic diversity : a GIScience contribution for the conservation of animal genetic resources

AncestrySNPminer: a bioinformatics tool to retrieve and develop ancestry informative SNP panels.

The Human Genome Initiative is an international research program for the creation of detailed genetic and physical maps of the human genome. Genome research projects generate enormous quantities of data. Database mining is the process of finding and extracting useful information from raw datasets. Computational genomics has identified a classification of three successive levels for the management and analysis of genetic data in scientific databases: Genomics. 1. Gene expression. 2. Proteomics. 3. Genome database mining is the identification of the protein-encoding regions of a genome and the assignment of functions to these genes on the basis of sequence similarity homologies against other genes of known function. Gene expression database mining is the identification of intrinsic patterns and relationships in transcriptional expression data generated by large-scale gene expression experiments. Proteome database mining is the identification of intrinsic patterns and relationships in translational expression data generated by large-scale proteomics experiments. Improvements in genome, gene expression and proteome database mining algorithms will enable the prediction of protein function in the context of higher order processes such as the regulation of gene expression, metabolic pathways and signalling cascades. Thus, the final objective of such higher-level functional analysis will be the elucidation of high-resolution structural and functional maps of the human genome. Contents

Sylvain Henry

Papers

Database Mining in the Human Genome Initiative