https://acawiki.org/api.php?action=feedcontributions&user=Hitwhicks&feedformat=atomAcaWiki - User contributions [en]2024-03-29T14:23:32ZUser contributionsMediaWiki 1.31.12https://acawiki.org/index.php?title=Detecting_recent_selective_sweeps_while_controlling_for_mutation_rate_and_background_selection&diff=11390Detecting recent selective sweeps while controlling for mutation rate and background selection2018-10-12T16:08:51Z<p>Hitwhicks: Created page with "{{Summary |title=Detecting recent selective sweeps while controlling for mutation rate and background selection |authors=Christian D. Huber, Michael DeGiorgio, Ines Hellmann,..."</p>
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<div>{{Summary<br />
|title=Detecting recent selective sweeps while controlling for mutation rate and background selection<br />
|authors=Christian D. Huber, Michael DeGiorgio, Ines Hellmann, Rasmus Nielsen<br />
|url=https://onlinelibrary.wiley.com/doi/full/10.1111/mec.13351<br />
|tags=molecular population genetics,<br />
|summary=The paper attempts to improve upon the existing composite likelihood model of Sweepfinder in detecting Hard sweeps (i.e., selection of rare beneficial mutations which lead to reduction in surrounding local variation). The authors trace the different models used to detect possible sweep sites. This includes:<br />
➢ using the entire genomic background as the null model with the alternate hypothesis as the Site Frequency Spectrum (SFS) under selection. The issues with these models were that although they were robust and computationally quick, it led to identification of higher false positives especially in populations that had gone through recent bottlenecks. Additionally, including the entire genomic background would also include invariable sites which could’ve risen due to selective constraint or reduced mutation rates, thus adding to more false positives.<br />
➢ Nielsen’s suggestion of including only polymorphic sites, leaving out the invariable ones. While this may certainly reduce false positives from above mentioned sources, even in polymorphic sites, background selection causes local reduction in neutral variation. Therefore, in Nielsen’s model, one would need to extensively model background selection, which is still a work in progress.<br />
Under the assumptions of a single population (complete genome of 9 unrelated European individuals) and considering mutations to have only recently reached fixation, the authors attempt to improve the robustness and simultaneously control for false positives by:<br />
i. Including invariant sites with fixed differences with an outgroup (under infinite sites model)<br />
ii. Including invariant sites including all polymorphic sites<br />
By including invariable sites that differ from an outgroup (Chimpanzees), effect of mutation rates across all sites are proportional and therefore does not affect the SFS. Another key component is the effect of background selection which the authors model using B value (a factor that specifies the effective population size after background selection). Subsequently, the authors are quick to point out the limitations of this approach given that B value estimates are available only for certain organisms. Additionally, the B values only consider the effect of Background selection on effective population size and not other factors such as allele frequency distribution. Coalescent simulations using this model was used to indicate higher detection power with age of mutation in the new models compared to the older ones. Additionally, they were also shown to be more robust under mutation variation and population bottleneck conditions. Finally, addressing False Positive Rates (FPRs) of sweep detection, a model including all sites generates higher FPRs compared to the model including just fixed differences with outgroup. Therefore, the authors suggest using the latter model where outgroup information is available.<br />
|relevance=Perspectives and Limitations<br />
While the authors show that the new model has improved performance in robustness and lower FPRs, it does so contingent on certain conditions. This includes factors such as outgroup information availability, bottleneck strength and time of occurrence, mutation rate and background selection, based on which the appropriate model needs to be chosen. The authors also put forward a new method of correcting for background selection using B value maps, helping us differentiate between neutral diversity and diversity because of sweeps. This also leads to lower FPRs compared to the HKA test. It needs to be noted though, as previously mentioned, that the B value maps are available only for certain organisms and only account for effect on Effective population size. More accurate models of background selection can lead to even lower FPRs.<br />
|journal=Molecular Ecology<br />
|pub_date=2015/08/20<br />
|doi=10.1111/mec.13351<br />
|subject=Biology<br />
}}</div>Hitwhickshttps://acawiki.org/index.php?title=An_Expanded_View_of_Complex_traits:_from_polygenic_to_omnigenic&diff=11363An Expanded View of Complex traits: from polygenic to omnigenic2018-05-25T15:28:40Z<p>Hitwhicks: An Expanded View of Complex traits: from polygenic to omnigenic</p>
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<div>{{Summary<br />
|title=An Expanded View of Complex traits: from polygenic to omnigenic<br />
|authors=Boyle EA, Li YI, Pritchard JK<br />
|url=https://www.cell.com/cell/abstract/S0092-8674(17)30629-3<br />
|tags=biology, genetics, population genetics, molecular population genetics, nature<br />
|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:<br />
> 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.<br />
> 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.<br />
> The quantitative effect of a given variant is dependent on cell type and would be calculated as average effect weighted by cell type significance.<br />
|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:<br />
> 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.<br />
> 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.<br />
> 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<br />
medicine where genotype-disease phenotypes correlations are utilized. <br />
<br />
Articles:<br />
1. https://www.sciencedaily.com/releases/2017/06/170620140627.htm<br />
2. http://www.epistasisblog.org/2017/06/is-gwas-hoax.html<br />
3. https://medcitynews.com/2017/06/genome-wide-association-studies-flawed/<br />
4. https://www.theatlantic.com/science/archive/2017/06/its-like-all-connected-man/530532/?utm_source=feed<br />
5. https://gizmodo.com/this-study-is-forcing-scientists-to-rethink-the-human-g-1796172648<br />
|journal=Cell, Volume 169, Issue 7, p1177–1186<br />
|pub_date=15 June 2017<br />
|doi=https://doi.org/10.1016/j.cell.2017.05.038<br />
|subject=Biology<br />
}}</div>Hitwhicks