This dataset comes from Human Ageing Genomic Resources (HAGR) which is a repository containing information about the genetics of human aging. Information is obtained from modern approaches such as functional genomics, network analyses, systems biology and evolutionary analyses.
The dataset includes all genes directly related to aging in humans plus the best candidate genes obtained from model organisms. Human genes are thus considerably better annotated and include more information. The dataset is manually curated by experts to ensure high-quality content.
This section of GenAge features genes possibly related to human aging. Briefly, genes were selected for inclusion based on findings in model organisms put in the context of human biology plus the few genes directly related to aging in humans. As such, genes should be seen as candidate human aging-associated genes. All entries are the result of an extensive review of the literature and feature considerable manually-curated annotation; the reason why each gene is featured in GenAge is given in each entry, in addition to other bibliographical references. GenAge also allowed the development of a system-level interpretation of aging which revealed that alterations to DNA are more relevant to aging than other forms of molecular damage.
For a gene to be featured, its association with aging and/or longevity must be unambiguous, and hence most genes were selected based on genetic manipulations and not mere correlations, such as a gene’s upregulation with age, in which causality is impossible to determine.
The human dataset in GenAge is a curated database of genes that may regulate human aging or that at least might be considerably associated with the human aging phenotype. It is a functional genomics database designed to provide up-to-date information in the context of aging and molecular genetics.
Because the focus is on the fundamental aging process, what some authors call senescence, and not just age-related pathologies, the human dataset features primarily genes related to biological aging rather than genes that only affect longevity by having an impact on overall health. This is an important point because longevity can be influenced by factors unrelated to aging, and the distinction is crucial, albeit often difficult. (For those interested in genes associated with human longevity, please refer to the LongevityMap). Likewise, a gene is differentially expressed during aging is not by itself proof that this gene is causally involved in the aging process. Nonetheless, for researchers studying transcriptional changes with age, also available are genes commonly differentially expressed during mammalian aging which were identified by performing a meta-analysis of aging microarray data.
Given the above considerations, when using the human dataset, it should not be expected to find genes solely associated with a given age-related pathology but rather genes that can regulate the aging process as a whole or at least multiple aspects of the aging phenotype. As mentioned above, genes in the human dataset are by and large selected based on findings in model organisms, and thus they must be classified as putative, not proven, cases of genes associated with human aging.
Each gene in the human dataset was selected after an extensive review of the literature. Identified genes were associated with aging in model organisms as well as those that may directly modulate aging in mammals, including humans. Of course, genes related to aging in model systems may or may not be related to human aging, and so the literature was reviewed concerning human and mouse homologs of genes identified in lower organisms.
Each gene was selected or excluded based on its association with aging in the different model systems, with priority being given to organisms biologically and evolutionary more closely related to humans. Because the focus is on the genetic basis of human aging, an in-depth description of aging in model systems was not provided but rather incorporated in the information gathered from multiple models to gather clues about the genetics of human aging.
Initially, the genes were grouped according to genes associated with organismal aging to obtain functional groups. These are groups of genes that share similar functions or are associated with similar pathways. Identifying the largest groups and those most strongly associated with aging allowed the selection of a number of other genes for inclusion in the human dataset due to their association with other genes or pathways previously linked to aging.
Information from several other databases was also evaluated and, in some cases, integrated into GenAge. Several genes only indirectly linked to aging are featured as a preference for false positives to false negatives; while users can ignore entries they consider irrelevant, false negatives can impact on research conducted using GenAge.