The candidate gene approach to conducting genetic association studies focuses on associations between genetic variation within pre-specified genes of interest, and phenotypes or disease states. This is in contrast to genome-wide association studies (GWAS), which is a hypothesis-free approach that scans the entire genome for associations between common genetic variants (typically SNPs) and traits of interest. Candidate genes are most often selected for study based on a priori knowledge of the gene's biological functional impact on the trait or disease in question. The rationale behind focusing on allelic variation in specific, biologically relevant regions of the genome is that certain alleles within a gene may directly impact the function of the gene in question and lead to variation in the phenotype or disease state being investigated. This approach often uses the case-control study design to try to answer the question, "Is one allele of a candidate gene more frequently seen in subjects with the disease than in subjects without the disease?" Candidate genes hypothesized to be associated with complex traits have generally not been replicated by subsequent GWASs or highly powered replication attempts. The failure of candidate gene studies to shed light on the specific genes underlying such traits has been ascribed to insufficient statistical power, low prior probability that scientists can correctly guess a specific allele within a specific gene that is related to a trait, poor methodological practices, and data dredging.
Suitable candidate genes are generally selected based on known biological, physiological, or functional relevance to the disease in question. This approach is limited by its reliance on existing knowledge about known or theoretical biology of disease. However, molecular tools are allowing insight into disease mechanisms and pinpointing potential regions of interest in the genome. Genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping examine common variation across the entire genome, and as such can detect a new region of interest that is in or near a potential candidate gene.
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Une étude d'association pangénomique (en anglais genome-wide association study, GWAS) est une analyse de nombreuses variations génétiques chez de nombreux individus, afin d'étudier leurs corrélations avec des traits phénotypiques. Ces études se concentrent généralement sur les associations entre les polymorphismes nucléotidiques (SNP) et des phénotypes tels que les maladies humaines majeures. En effet, quand elle est appliquée sur des données humaines, une comparaison de séquences d’ADN se fait entre individus ayant plusieurs phénotypes différents pour un même caractère, la taille par exemple.
Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait more often than would be expected by chance occurrence. Studies of genetic association aim to test whether single-locus alleles or genotype frequencies or more generally, multilocus haplotype frequencies differ between two groups of individuals usually diseased subjects and healthy controls). Genetic association studies are based on the principle that genotypes can be compared "directly", i.e.
Se penche sur les réseaux de régulation des gènes, les techniques ChIP-seq, l'interprétation des données du projet ENCODE et la modélisation de l'expression génique différentielle.
Explore la médecine personnalisée, la variabilité génétique et la réponse aux médicaments, en mettant l'accent sur l'adaptation des traitements en fonction des profils génétiques et métaboliques individuels.
Evolutionary diversity in species can arise in many ways, including local adaptation. Despite the global importance of tropical forest ecosystems, few studies have explored patterns of local adaptation in tropical tree species. We investigated population g ...
OXFORD UNIV PRESS2022
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Background Alzheimer's disease (AD) is a complex disorder caused by a combination of genetic and non-genetic risk factors. In addition, an increasing evidence suggests that epigenetic mechanisms also accompany AD. Genetic and epigenetic factors are not ind ...
BMC2020
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Samβada is a genome‐environment association (GEA) software, designed to search for signatures of local adaptation. However, pre‐ and post‐processing of data can be labour‐intensive, preventing wider uptake of the method. We have now developed R.SamBada, an ...