A case-control collapsing analysis identifies epilepsy genes implicated in trio sequencing studies focused on de novo mutations

Trio exome sequencing has been successful in identifying genes with de novo mutations (DNMs) causing epileptic encephalopathy (EE) and other neurodevelopmental disorders. Here, we evaluate how well a case-control collapsing analysis recovers genes causing dominant forms of EE originally implicated by DNM analysis. We performed a genome-wide search for an enrichment of "qualifying variants" in protein-coding genes in 488 unrelated cases compared to 12,151 unrelated controls. These "qualifying variants" were selected to be extremely rare variants predicted to functionally impact the protein to enrich for likely pathogenic variants. Despite modest sample size, three known EE genes (KCNT1, SCN2A, and STXBP1) achieved genome-wide significance (p<2.68×10−6). In addition, six of the 10 most significantly associated genes are known EE genes, and the majority of the known EE genes (17 out of 25) originally implicated in trio sequencing are nominally significant (p<0.05), a proportion significantly higher than the expected (Fisher’s exact p = 2.33×10−17). Our results indicate that a case-control collapsing analysis can identify several of the EE genes originally implicated in trio sequencing studies, and clearly show that additional genes would be implicated with larger sample sizes. The case-control analysis not only makes discovery easier and more economical in early onset disorders, particularly when large cohorts are available, but also supports the use of this approach to identify genes in diseases that present later in life when parents are not readily available

Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated <i>Caenorhabditis elegans</i> strains

<div><p>Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute and is instead dependent upon multiple factors such as the age and genetic background of an organism. In order to understand the mechanistic basis of these changes, we characterized heritable trait differences between two domesticated strains of <i>C</i>. <i>elegans</i>. We previously identified a major effect locus, caused in part by a mutation in a component of the NURF chromatin remodeling complex, that regulates reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animal’s reproductive lifespan. Here, we use a previously published macroscale model of the egg-laying rate in <i>C</i>. <i>elegans</i> to show that time-dependent effect-size is explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate key predictions of this model with controlled mating experiments and quantification of oogenesis and sperm use. Incorporation of this model into QTL mapping allows us to identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of the unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.</p></div

Additional modifier QTLs affect egg-laying in an age-dependent manner.

<p><b>A</b>. QTL mapping using the <i>nurf-1</i> genotype as an additive (black) or interactive (green) covariate. Black and green stars indicate the five QTLs with genome-wide significance above 0.05. <b>B.</b> Average egg-laying rate of data from 94 RILs used for QTL mapping in panel <b>A</b> partitioned and averaged by their genotype at <i>nurf-1</i> and one of the modifier QTLs at five time points. The circle indicates the average egg-laying rate of all of the RILs that share the particular genotype. The plus signs indicate the standard error. The modifier QTL for each row is shown in the upper left of the first panel of each row, and its genotype is indicated on the x-axis. The <i>nurf-1</i> genotype is indicated by the color of the lines. A non-white background coloring indicates a significant effect of the modifier QTL by ANOVA (p<0.05). A yellow background indicates a significant effect of the modifier QTL but no significant non-linear interaction with <i>nurf-1</i> by ANOVA (p < 0.05). A blue background indicates a significant effect of the modifier QTL and a significant positive non-linear interaction with <i>nurf-1</i> by ANOVA (p < 0.05). A red background indicates a significant effect of the modifier QTL and a significant negative non-linear interaction with <i>nurf-1</i> by ANOVA (p < 0.05) For modifier QTLs with a significant effect, we also included the amount of variance explained by that locus in the bottom left or bottom right corner of the panel.</p

A major-effect QTL has an age-dependent effect on egg-laying.

<p><b>A.</b> History of two laboratory strains of <i>C</i>. <i>elegans</i> (N2 and LSJ2) following isolation of a single hermaphrodite individual from mushroom compost collected in Bristol, England in 1951. LSJ2 was grown in liquid, axenic culture whereas N2 was propagated on agar plates. <b>B.</b> Schematic of CX12311 and NIL_{<i>nurf-1</i>} strains. CB4856 is a wild strain isolated from Hawaii. N2 contains two fixed mutations in the <i>npr-1</i> and <i>glb-5</i> genes. To avoid studying their effects, we backcrossed the ancestral alleles of these genes from CB4856 into the N2 strain. NIL_{<i>nurf-1</i>} contains a small region surrounding <i>nurf-1</i> backcrossed from LSJ2 into CX12311. <b>C</b>. Schematic of the experiments used to characterize the egg-laying rate at five time points. t = 0 was defined as the start of the L4 stage. <b>D. Top panel</b>. All egg-laying rate data of 94 RIL strains created between the CX12311 and LSJ2 strains. Animals were partitioned based upon their <i>nurf-1</i> genotype (blue = N2; red = LSJ2). The small difference in x-axis values for the two backgrounds are for illustration purposes only and do not indicate differences between measurements of egg-laying rate. Overlaid is a boxplot showing the quartiles of the data (the box) with the whiskers extended to show the rest of the distribution except for points determined to be outliers. All scatterplots and boxplots in the subsequent figures were calculated in the same way. For all figures, ns p >0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 by Mann-Whitney U test with Bonferroni correction. <b>Bottom panel.</b> The effect size of the <i>nurf-1</i> locus measured from the RIL strains. <b>E. Top panel.</b> The egg-laying rate of CX12311, LSJ2, and NIL_{<i>nurf-1</i>} strain measured at five time points. For just this figure, only non-significant differences are shown. All other comparisons are significant at p < 0.05. <b>Bottom panel</b>. Effect size of the <i>nurf-1</i> locus measured from the NIL and parental strains.</p

Analysis of components of egg-laying.

<p><b>A.</b> Schematic of the <i>C</i>. <i>elegans</i> gonad. Germline Stem Cells (not shown due to their large number) self-renew in the mitotic zone. As they migrate away from the stem cell niche, they undergo meiosis and differentiate into mature oocytes. Ovulation forces the primary oocyte into the spermatheca, which stores previously produced self-sperm, where it is fertilized and develops an eggshell. Fertilized eggs develop in the uterus until they are laid through the vulva. Only one of two gonads is shown. <b>B</b>. Number of fertilized eggs in the uterus as determined by DIC microscopy. <b>C.</b> Number of large oocytes as determined by DAPI staining and fluorescent microscopy. <b>D.</b> Number of germline progenitor cells. <b>E.</b> Number of cells undergoing mitosis in the mitotic zone, as determined by immunofluorescence to a post-translational modification (H3S10P) in Histone 3 correlated with chromatin condensation in mitosis.</p

QTL mapping of parameters estimated for each RIL strain.

<p><b>A and B.</b> A least-squares method was used to fit individual k_o and S₀ values for all 94 RIL strains. <b>Left panel:</b> Histogram of all k_o and S₀ values from 94 RIL strains. <b>Middle panel:</b> QTL mapping of the k_o and S₀ parameters. The black line indicates a one-dimensional scan. We also used the <i>nurf-1</i> genotype as an additive (blue) or interactive (red) covariate. Black stars indicate QTLs with genome-wide significance above 0.05. <b>Right panel:</b> RIL strains segregated by their genotypes at the <i>nurf-1</i> locus (panel <b>A</b>) or their genotypes at the <i>nurf-1</i> and QTL_V loci (panel <b>B</b>). <b>C.</b> Schematic of NIL strains used for panel <b>D</b>. <b>D.</b> Total fecundity (number of eggs laid over the animal’s lifespan) of NIL strains and parental strains indicated on the x-axis.</p

Reduction in the egg-laying rate at later time points caused by the use of self-sperm.

<p><b>A.</b> Number of fertilized eggs and large oocytes at the 66-hour time point as determined by DIC microscopy and DAPI staining. This analysis indicates CX12311, which shows a reduced egg-laying rate at this time point, is not retaining fertilized eggs in the uterus nor displaying a loss in oocytes. <b>B</b>. Modeling (identical to <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006769#pgen.1006769.g003" target="_blank">Fig 3</a>) of the remaining number of self-sperm. This model predicts CX12311 will have fewer self-sperm than NIL_{<i>nurf-1</i>} at later points in life. <b>C</b>. Measurement of the remaining number of sperm in CX12311 and NIL_{<i>nurf-1</i>} as determined by DAPI staining and fluorescence microscopy. These results are consistent with the predictions from panel <b>B</b>. <b>D.</b> Egg-laying rate of CX12311 and NIL_{<i>nurf-1</i>} hermaphrodites mated or unmated to CX12311 males. Mating increases the number of sperm stored in the spermatheca. These results indicate the sign-switching observed in unmated animals (48 hours) can be reversed by the addition of sperm. The decrease in egg-laying rate in both strains at later time points (88 hours) can also be reversed by the addition of sperm.</p

Macroscopic modeling of the effect of the <i>nurf-1</i> locus on egg-laying.

<p><b>A.</b> Illustration of negative feedback in the egg-laying process in <i>C</i>. <i>elegans</i>. A limited number of sperm (200–350) are initially created and stored in the spermatheca before the gonad switches to exclusively produce oocytes. Sperm release a hormone called MSP, which induces oocytes to ovulate and enter the spermatheca where they are fertilized. Fertilized eggs then enter the uterus where they are temporarily stored prior to egg-laying. <b>B.</b> Published macroscopic model of the egg-laying process. Ordinary differential equations (ODEs) describing the relationship between fertilized eggs (E), sperm (S), and oocytes (O) while initial conditions are given on the right. A prime (‘) indicates a time derivative. <b>C.</b> Fit of the model from Fig 3B to the data plotted in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006769#pgen.1006769.g001" target="_blank">Fig 1E</a> using a common value of the k_f parameter for all samples (0.00026). Top value shows best fit values for S₀. Bottom panel shows best fit values for k_o. <b>D</b>. Predicted egg-laying rate and effect-size calculated for CX12311, LSJ2, and NIL_{<i>nurf-1</i>} strains using the average value of the parameters plotted in Fig 3C. This model can account for the rise and fall of egg-laying rate and the change in effect-size over time.</p