Genetic epidemiology, genetic maps and positional cloning

Differential admixture in Latin American populations and its impact on the study of colorectal cancer

Genome-wide association studies focused on searching genes responsible for several diseases. Admixture mapping studies proposed a more efficient alternative capable of detecting polymorphisms contributing with a small effect on the disease risk. This method focuses on the higher values of linkage disequilibrium in admixed populations. To test this, we analyzed 10 genomic regions previously defined as related with colorectal cancer among nine populations and studied the variation pattern of haplotypic structures and heterozygosity values on seven categories of SNPs. Both analyses showed differences among chromosomal regions and studied populations. Admixed Latin-American samples generally show intermediate values. Heterozygosity of the SNPs grouped in categories varies more in each gene than in each population. African related populations have more blocks per chromosomal region, coherently with their antiquity. In sum, some similarities were found among Latin American populations, but each chromosomal region showed a particular behavior, despite the fact that the study refers to genes and regions related with one particular complex disease. This study strongly suggests the necessity of developing statistical methods to deal with di- or tri-hybrid populations, as well as to carefully analyze the different historic and demographic scenarios, and the different characteristics of particular chromosomal regions and evolutionary forces.

Prioritization of positional candidate genes using multiple web-based software tools

The prioritization of genes within a candidate genomic region is an important step in the identification of causal gene variants affecting complex traits. Surprisingly, there have been very few reports of bioinformatics tools to perform such prioritization. The purpose of this article is to investigate the performance of 3 positional candidate gene software tools available, PosMed, GeneSniffer and SUSPECTS. The comparison was made for 40, 20 and 10 Mb regions in the human genome centred around known susceptibility genes for the common diseases breast cancer, Crohn's disease, age-related macular degeneration and schizophrenia. The known susceptibility gene was not always ranked highly, or not ranked at all, by 1 or more of the software tools. There was a large variation between the 3 tools regarding which genes were prioritized, and their rank order. PosMed and GeneSniffer were most similar in their prioritization gene list, whereas SUSPECTS identified the same candidate genes only for the narrowest (10 Mb) regions. Combining 2 or all of the candidate gene finding tools was superior in terms of ranking positional candidates. It is possible to reduce the number of candidate genes from a starting set in a region of interest by combining a variety of candidate gene finding tools. Conversely, we recommend caution in relying solely on single positional candidate gene prioritization tools. Our results confirm the obvious, that is, that starting with a narrower positional region gives a higher likelihood that the true susceptibility gene is selected, and that it is ranked highly. A narrow confidence interval for the mapping of complex trait genes by linkage can be achieved by maximizing marker informativeness and by having large samples. Our results suggest that the best approach to classify a minimum set of candidate genes is to take those genes that are prioritized by multiple prioritization tools.

Beyond race: towards a whole-genome perspective on human populations and genetic variation

The renewed emphasis on population-specific genetic variation, exemplified most prominently by the International HapMap Project, is complicated by a longstanding, uncritical reliance on existing population categories in genetic research. Race and other pre-existing population definitions (ethnicity, religion, language, nationality, culture and so on) tend to be contentious concepts that have polarized discussions about the ethics and science of research into population-specific human genetic variation. By contrast, a broader consideration of the multiple historical sources of genetic variation provides a whole-genome perspective on the ways i n which existing population definitions do, and do not, account for how genetic variation is distributed among individuals. Although genetics will continue to rely on analytical tools that make use of particular population histories, it is important to interpret findings in a broader genomic context.