Examine individual changes

Abuse Filter navigation (Home | Recent filter changes | Examine past edits | Abuse Log)
Jump to: navigation, search

This page allows you to examine the variables generated by the Abuse Filter for an individual change, and test it against filters.

Variables generated for this change

Edit count of user (user_editcount)
Name of user account (user_name)
Page ID (article_articleid)
Page namespace (article_namespace)
Page title (without namespace) (article_text)
An Expanded View of Complex traits: from polygenic to omnigenic
Full page title (article_prefixedtext)
An Expanded View of Complex traits: from polygenic to omnigenic
Action (action)
Edit summary/reason (summary)
An Expanded View of Complex traits: from polygenic to omnigenic
Whether or not the edit is marked as minor (minor_edit)
Old page wikitext, before the edit (old_wikitext)
New page wikitext, after the edit (new_wikitext)
{{Summary |title=An Expanded View of Complex traits: from polygenic to omnigenic |authors=Boyle EA, Li YI, Pritchard JK |url=https://www.cell.com/cell/abstract/S0092-8674(17)30629-3 |tags=biology, genetics, population genetics, molecular population genetics, nature |summary=The paper attempts to explain what is considered as “sensible” source of complex trait variation. In doing so, it finds a half way point between Mendelian genetics and Fischer’s infinitesimal model with a categorization of genes and their variants into core and peripheral effectors for complex traits. It links the fruits of GWAS (Genome Wide Association Studies) to aspects such as network theory to redefine “Total” trait heritability as a confluence of certain “causative” genes acting on a background of broader genetic expression. This was explained in part, by the 100 kb window analysis for effect genes based on Linkage Disequilibrium (LD) of variants in GWAS for traits such as height and disease traits like Schizophrenia. The authors do not get carried away by the broad expression profile of variance and are careful to point out enrichment of variants in genes associated specifically to the trait or disease. The effect of large effect size rare variants is also described in the context of Schizophrenia where a difference in the outcome of the rare variant studies and 108-GW significant loci study are indicated. The GWAS which was based on common variants does not pick up on rare variants and so the authors are quick to point out that these rare variants may play a more significant and direct role in Schizophrenia than the common variants. While this reinforces their idea of a set of core genes for complex traits, the number of GWAS cited proves to be a potential limitation. The perspectives begin to come together with the paper laying the foundation for “Omnigenecity” stating that, “any gene with regulatory variants..........nontrivial effects on risk for that disease”. Under the umbrella of Omnigenic complex traits, the authors make the following key posits: > Categorization of genes into “core” and “peripheral” with any changes to core genes having the largest effect sizes on the trait/disease phenotype while maintaining that they only modestly contribute to total trait heritability. Examples stated include role of possible “core” genes, IRX3 and IRX5 in adipocyte differentiation and subsequently in obesity. > Complexity of network interconnectedness to explain broader gene expression affecting regulation and expression of core genes. This was backed by current evidence from the “small world” proponent of Network theory. In addition, a biological model involving Trans-acting eQTL’s (expression Quantitative Trait Loci) connecting regulatory variants to mRNA heritability and protein networks was proposed. > The quantitative effect of a given variant is dependent on cell type and would be calculated as average effect weighted by cell type significance. |relevance=This paper gives an alternate distinct framework with the underlying principle that in biological networks, some connections are more important than others. That being said, it is of the view that "everything affects everything". This means under this view, GWAS require an updated approach to find any significant correlation between genotype-phenotype. This paper does come with its caveats though: > The use of the term “causal variants” by the authors is not accurate when discussing GWAS as these studies indicate correlation and not causation. Mechanistic elucidation is required to indicate causation. > Outcomes of Active chromatin region analysis of any cell within a cell type group is extrapolated to the entire cell type group. This may however not be fair given that single cell data now reveals distinction between cells even within narrow cell type groups. > While the binary classification of genes as relevant or irrelevant to trait/disease phenotype may not be fair with our current understanding, the authors propose a graduated scale to define core and peripheral genes. This is vague and may not be practically useful in cases such as Personalized medicine where genotype-disease phenotypes correlations are utilized. Articles: 1. https://www.sciencedaily.com/releases/2017/06/170620140627.htm 2. http://www.epistasisblog.org/2017/06/is-gwas-hoax.html 3. https://medcitynews.com/2017/06/genome-wide-association-studies-flawed/ 4. https://www.theatlantic.com/science/archive/2017/06/its-like-all-connected-man/530532/?utm_source=feed 5. https://gizmodo.com/this-study-is-forcing-scientists-to-rethink-the-human-g-1796172648 |journal=Cell, Volume 169, Issue 7, p1177–1186 |pub_date=15 June 2017 |doi=https://doi.org/10.1016/j.cell.2017.05.038 |subject=Biology }}
Unix timestamp of change (timestamp)