Sad Background Video Effects Free Download

The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist or about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scalloped(E3) allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-by-introgression experiment and a tagged RNA gene expression dataset. In addition we used whole genome resequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative noncomplementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well.

Background effects occur when the same spontaneous or induced mutations show different phenotypic effects across genetically distinct individuals1,2,3,4,5,6,7. Countless examples of background effects have been described across species and traits1,2, collectively suggesting that this phenomenon is common in biological systems and plays a significant role in many phenotypes. For example, alleles that show background effects contribute to a wide range of hereditary disorders, including, but not limited to, colorectal cancer, hypertension, and phenylketonuria8. Background effects may also impact other disorders that frequently involve de novo mutations, such as autism9, congenital heart disease10, and schizophrenia11. Additionally, it has been proposed that background effects can shape the potential trajectories of evolutionary adaptation12,13, influence the emergence of novel traits7, and help maintain deleterious genetic variation within populations14.

Download File 🔥 https://tinurll.com/2y3LzO 🔥

In this paper, we perform a detailed genetic characterization of a number of background effects across multiple environments. Previous work in yeast, as well as other model species, has established that mutations in chromatin regulation and transcription often show background effects5,6,7,21,28,29,33. We extend this past work by knocking out seven different chromatin regulators in a cross of the BY4716 (BY) and 322134S (3S) strains of Saccharomyces cerevisiae. We generate and genotype 1411 wild-type and knockout segregants, measure the growth of these individuals in 10 environments, and perform linkage mapping with these data. In total, we identify 1086 interactions between the knockouts and segregating loci. These interactions allow us to obtain novel, detailed insights into the genetic architecture of background effects across different mutations and environments.

Examples of mutation-responsive genetic effects. a shows representative examples of one-, two-, and three-locus mutation-responsive effects with larger phenotypic effects in wild-type segregants than mutants. In contrast, b shows representative examples of one-, two-, and three-locus mutation-responsive effects with larger phenotypic effects in mutants than wild-type segregants. Means depicted along the y axis show residuals from a fixed-effects linear model that includes the mutation-independent effect of each involved locus, as well as any possible lower-order mutation-independent and mutation-responsive effects. The different genotype classes are plotted below the x axis. Blue and orange boxes correspond to the BY and 3S alleles of a locus, respectively. Error bars represent one standard deviation from the mean

Most mutation-responsive genetic effects involve multiple loci. In a, the number of mutation-independent and mutation-responsive genetic effects detected in each environment are shown. In b, the aggregate numbers of mutation-responsive effects found for each knockout across the 10 environments are provided

While only 29% (36 of 125) of the mutation-independent effects involved multiple loci, this proportion was more than tripled (89%; 965 of 1086) among the mutation-responsive effects (Fig. 2a). Simulations indicate that our statistical power to detect mutation-responsive loci was appreciably higher for single locus effects than for multiple locus effects, suggesting that our results may underestimate the importance of higher-order epistasis to background effects (Supplementary Fig. 7). To better assess how loci involved in the identified higher-order interactions contribute to background effects, we partitioned the individual and joint contributions of involved loci to mutation-responsive phenotypic variance (Supplementary Data 4 and 5; Methods). For mutation-responsive two-locus effects, on average, 78% of the mutation-responsive phenotypic variance was attributed to the higher-order interaction between the knockout and both loci (Fig. 3a). Likewise, among mutation-responsive three-locus effects, on average, 58% of the mutation-responsive phenotypic variance was explained by the higher-order interaction of the knockout and the three loci (Fig. 3b). Thus, most mutation-responsive effects involve multiple loci that contribute to background effects predominantly through their higher-order interactions with each other and a mutation, rather than through their individual interactions with a mutation.

Analysis of mutation-responsive effects across environments. The height of each stacked bar indicates the number of mutation-responsive effects that were detected in a given environment. The bars are color-coded according to the number of additional environments in which these mutation-responsive effects could be detected when liberal statistical thresholds were employed (Methods)

Analysis of mutation-responsive effects across knockout backgrounds. In a, the number of mutation-responsive effects that interacted with only one knockout (pink) or interacted with multiple knockouts (blue) are shown for each knockout. In b, the phenotypic variance explained (PVE) for each mutation-responsive effect is shown in the relevant knockout (KO) segregants, as well as in the wild-type (WT) segregants. The PVE for each mutation-responsive effect was determined using fixed-effects linear models fit within each individual background (Methods). Mutation-responsive effects are color-coded by the knockout population in which they were identified. In c, the percentage of mutation-responsive effects that showed larger phenotypic effects in mutants than in wild-type segregants (y axis, left side) and mutation-responsive effects that showed larger phenotypic effects in wild-type segregants than in mutants (y axis, right side) is depicted. These values are plotted as a function of the number of knockouts that interact with a given mutation-responsive effect. Error bars represent 95% bootstrap confidence intervals (Methods)

In addition to advancing understanding of background effects, our results may also have more general implications for the genetic architecture of complex traits. Many phenotypes, including common disorders like autism9 and schizophrenia11, are influenced by loss-of-function mutations that occur de novo or persist within populations at low frequencies. We have shown that these mutations can significantly change the phenotypic effects of many polymorphisms within a population by altering how these polymorphisms interact with each other and the environment. Although these complicated interactions between mutations, standing polymorphisms, and the environment are often ignored in genetics research, our study suggests that they in fact play a major role in determining the relationship between genotype and phenotype.

All genetic mapping was conducted within each environment using fixed-effects linear models applied to the complete set of 1411 wild-type and knockout segregants. To ensure that mean differences in growth among the eight backgrounds were always controlled for during mapping, we included a background term in our models. Throughout the paper, we refer to loci or combinations of loci that statistically interact or do not statistically interact with the background term as mutation-responsive and mutation-independent, respectively. Genetic mapping was performed in R using the lm() function, with the p values for relevant terms obtained from tables generated using the summary() function.

In the aforementioned linkage scans, genetic effects exhibited statistical interactions with the background term if they had a different phenotypic effect in at least one of the eight backgrounds relative to the rest. To determine the specific knockouts that interacted with each mutation-responsive effect, we used the contrast() function from the R package lsmeans. This was applied to the specific effect of interest post hoc using the same linear models that were employed for detection. All possible pairwise contrasts between wild-type and knockout segregants were conducted. Mutation-responsive effects were assigned to specific mutations if the contrast between a mutation and a WT population was nominally significant. Unless otherwise noted, we counted each assignment of a mutation-responsive effect to a specific knockout as a separate genetic effect even if they involved the same set of loci. 2351a5e196