Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates.
Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal.
Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently.
Natural selection exerts its influence by changing allele frequencies at genomic polymorphisms. Alleles associated with harmful traits decrease in frequency while those associated with beneficial traits become more common. In a simple case, selection acts on a trait controlled by a single polymorphism; a large change in allele frequency at this polymorphism can eliminate a deleterious phenotype from a population or fix a beneficial one.
In recent years, several so-called next-generation DNA sequencing
platforms have begun to challenge the well-established Sanger sequencing
method. In two important ways—cost and speed—these next-gen
technologies provide improvements over Sanger sequencing...
It was shown recently by Homer and colleagues that it may be possible to determine whether a person with known genotypes at a number of markers was part of a pool of DNA from which only frequencies of alleles at the markers are known. In this study, we quantify how well such identification can work in practice. The larger the size of the sample from which the allele frequencies are available, the more independent genetic markers are required to allow individual identification.
In this report, we evaluate a recently-published method for resolving whether individuals are present in a complex genomic DNA mixture. Based on the intuition that an individual will be genetically “closer” to a sample containing him than to a sample not, the method investigated here uses a distance metric to quantify the similarity of an individual relative to two population samples.
G2P Knowledge Centre is part of GEN2PHEN and funded by the Health Thematic Area of the Cooperation Programme of the European Commission
within the VII Framework Programme for Research and Technological Development.