Family-based Data Research Articles

The Future of Genetic Studies of Complex Human Diseases

With advances in the human genome project and the increasing availability of deoxyribonucleic acid (DNA) markers scattered throughout the genome, such as simple sequence polymorphisms, variable number tandem repeats, and short sequence repeat polymorphisms, it has become increasingly possible to search for the genetic basis of complex human diseases using genome-wide screening methods.1-3 Linkage analysis using log odds (LOD) score analysis in large pedigrees has been the traditional tool to identify gene loci for human disorders, both for single-gene disorders (for example, Huntington's disease4) and for complex chronic diseases (such as bipolar disease5). Recently, Risch and Merikangas6 have argued that the future of genetic studies of complex human disease may depend, to a large extent, on applications of new association-type methods to familybased data.7 The main method of interest is the trans? mission disequilibrium test (TDT), in which alleles at a given locus for a person with a specific disease are compared with parental nontransmitted alleles to look for evidence of deviation from expectations in the ab? sence of linkage.8 The TDT has been shown to be a valid test of linkage in the presence of linkage disequilibrium (which creates associations with specific alleles). They showed that the TDT has more power than traditional linkage analysis for disease genes with weak to moderate effects on disease risks. In this paper, we argue that the future of the genetic study of complex disorders will rely increasingly on the classical epidemiologic association paradigm. We show that in the long run, improvements in study designs and in adjusting for population stratification using interviews and genetic markers will lead to a new era of populationbased incident case-control studies that could have more power, can lead to more detailed information on the presence or absence of a disease susceptibility gene, and define the magnitude of risks and gene-environment interaction, a crucial first step to disease prevention and health promotion.

Association Mapping of Disease Loci, by Use of a Pooled DNA Genomic Screen

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification. These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.

Monitoring of sewage outfalls in northern spain: preliminary studies of benthic communities

Baseline studies constitute, at the same time, the base and the reference point for the development of site-specific monitoring programs. As a previous step in the analysis of the structure of benthic communities in the infralitoral off Avilés and Gijón, multivariate techniques were used to assess the agreement between the distribution pattern of samples, from both rocky and soft bottom benthos, according to their species or family compositions. Sampling was carried out in summer 1992. Stations were located on a depth gradient basis (A: −1 m; B: −5 m; C: −15 m; D: −25 m; E: −35 m; F: −45 m) along eight transect lines. Abundance data from replicate samples from each station, aggregated at both the species and the family levels, were tested by three complementary multivariate measures of community structure: clustering (CL), principal components analyses (PCA) and multidimensional scaling (MDS) techniques. The main results obtained in this study demonstrated the adequacy of family-based abundance data for carrying out classification and ordination of samples from both rocky and soft bottom benthic ecosystems.

Monitoring of sewage outfalls in Northern Spain: Preliminary studies of benthic communities

Baseline studies constitute, at the same time, the base and the reference point for the development of sitespecific monitoring programs. As a previous step in the analysis of the structure of benthic communities in the infralitoral off Avilés and Gijón, multivariate techniques were used to assess the agreement between the distribution pattern of samples, from both rocky and soft bottom benthos, according to their species or family compositions.Sampling was carried out in summer 1992. Stations were located on a depth gradient basis (A: −1 m; B: −5 m; C: −15 m; D: −25 m; E: −35 m; F: −45 m) along eight transect lines. Abundance data from replicate samples from each station, aggregated at both the species and the family levels, were tested by three complementary multivariate measures of community structure: clustering (CL), principal components analyses (PCA) and multidimensional scaling (IvDS) techniques.The main results obtained in this study demonstrated the adequacy of family-based abundance data for carrying out classification and ordination of samples from both rocky and soft bottom benthic ecosystems.

Forty years family-based morbidity data in general practice.

Forty years family-based morbidity data in general practice.