ArticlePloS one2014
Human genome variation and the concept of genotype networks.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Genome-wide copy number variant screening of Saudi schizophrenia patients reveals larger deletions in cases versus controls.Frontiers in molecular neuroscience · 2023Article
- The ensemble of gene regulatory networks at mutation-selection balance.Journal of the Royal Society, Interface · 2023Article
- Characterizing the effect of background selection on the polygenicity of brain-related traits.Genomics · 2021Article
- Adding levels of complexity enhances robustness and evolvability in a multilevel genotype-phenotype map.Journal of the Royal Society, Interface · 2018Article
- Parallel or convergent evolution in human population genomic data revealed by genotype networks.BMC evolutionary biology · 2016Article
- Evolution on neutral networks accelerates the ticking rate of the molecular clock.Journal of the Royal Society, Interface · 2015Article
- toyLIFE: a computational framework to study the multi-level organisation of the genotype-phenotype map.Scientific reports · 2014Article
- Phenotypic Novelty in EvoDevo: The Distinction Between Continuous and Discontinuous Variation and Its Importance in Evolutionary Theory.Evolutionary biologyArticle
- Impacts of a biobank: Bridging the gap in translational cancer medicine.Indian journal of medical and paediatric oncology : official journal of Indian Society of Medical & Paediatric OncologyReview
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genotype networks are a concept used in systems biology to study sets of genotypes having the same phenotype, and the ability of these to bring forth novel phenotypes. In the past they have been applied to determine the genetic heterogeneity, and stability to mutations, of systems such as metabolic networks and RNA folds. Recently, they have been the base for reconciling the neutralist and selectionist views on evolution. Here, we adapted this concept to the study of population genetics data. Specifically, we applied genotype networks to the human 1000 genomes dataset, and analyzed networks composed of short haplotypes of Single Nucleotide Variants (SNV). The result is a scan of how properties related to genetic heterogeneity and stability to mutations are distributed along the human genome. We found that genes involved in acquired immunity, such as some HLA and MHC genes, tend to have the most heterogeneous and connected networks, and that coding regions tend to be more heterogeneous and stable to mutations than non-coding regions. We also found, using coalescent simulations, that regions under selection have more extended and connected networks. The application of the concept of genotype networks can provide a new opportunity to understand the evolutionary processes that shaped our genome. Learning how the genotype space of each region of our genome has been explored during the evolutionary history of the human species can lead to a better understanding on how selective pressures and neutral factors have shaped genetic diversity within populations and among individuals. Combined with the availability of larger datasets of sequencing data, genotype networks represent a new approach to the study of human genetic diversity that looks to the whole genome, and goes beyond the classical division between selection and neutrality methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.