RESUMO
Single nucleotide polymorphisms (SNPs) that lead to non-synonymous changes in proteins may have functional effects and be subject to selection. Hence they are of particular interest in the study of genetic diseases. We have genotyped approximately 28,000 such SNPs in three ethnic populations (the HapMap plates) and ten primate species and analyzed these data for evidence of selection. We find SNPs predicted by PolyPhen to be damaging, have lower allele frequencies, and are particularly likely to be population-specific. We have also grouped SNPs by molecular function or biological process of the associated genes and find evidence that selection may be acting in concert on classes of genes.
Assuntos
Povo Asiático/genética , População Negra/genética , Bases de Dados Genéticas , Polimorfismo de Nucleotídeo Único/genética , População Branca/genética , Animais , Frequência do Gene , Genética Populacional , Humanos , Primatas/genética , Seleção GenéticaRESUMO
High-throughput genotyping technologies that enable large association studies are already available. Tools for genotype determination starting from raw signal intensities need to be automated, robust, and flexible to provide optimal genotype determination given the specific requirements of a study. The key metrics describing the performance of a custom genotyping study are assay conversion, call rate, and genotype accuracy. These three metrics can be traded off against each other. Using the highly multiplexed Molecular Inversion Probe technology as an example, we describe a methodology for identifying the optimal trade-off. The methodology comprises: a robust clustering algorithm and assessment of a large number of data filter sets. The clustering algorithm allows for automatic genotype determination. Many different sets of filters are then applied to the clustered data, and performance metrics resulting from each filter set are calculated. These performance metrics relate to the power of a study and provide a framework to choose the most suitable filter set to the particular study.
Assuntos
Algoritmos , Técnicas de Sonda Molecular , Polimorfismo de Nucleotídeo Único/genética , Projetos de Pesquisa , Análise por Conglomerados , Simulação por Computador , Marcadores Genéticos/genética , Genótipo , Reprodutibilidade dos TestesRESUMO
Detection of genomic copy number changes has been an important research area, especially in cancer. Several high-throughput technologies have been developed to detect these changes. Features that are important for the utility of technologies assessing copy number changes include the ability to interrogate regions of interest at the desired density as well as the ability to differentiate the two homologs. In addition, assessing formaldehyde fixed and paraffin embedded (FFPE) samples allows the utilization of the vast majority of cancer samples. To address these points we demonstrate the use of molecular inversion probe (MIP) technology to the study of copy number. MIP is a high-throughput genotyping technology capable of interrogating >20 000 single nucleotide polymorphisms in the same tube. We have shown the ability of MIP at this multiplex level to provide copy number measurements while obtaining the allele information. In addition we have demonstrated a proof of principle for copy number analysis in FFPE samples.
Assuntos
Alelos , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Polimorfismo de Nucleotídeo Único , Linhagem Celular Tumoral , Pré-Escolar , Biologia Computacional , Fixadores , Formaldeído/química , Dosagem de Genes , Humanos , Lactente , Masculino , Sondas Moleculares/química , Análise de Sequência com Séries de Oligonucleotídeos/normas , Inclusão em Parafina , Reação em Cadeia da Polimerase , Polimorfismo de Fragmento de Restrição , Padrões de ReferênciaRESUMO
The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.
