Two-Stage Genome-Wide Search for Epistasis with Implementation to Recombinant Inbred Lines (RIL) Populations

<div><p>Objective and Methods</p><p>This paper proposes an inegrative two-stage genome-wide search for pairwise epistasis on expression quantitative trait loci (eQTL). The traits are clustered into multi-trait complexes that account for correlations between them that may result from common epistasis effects. The search is done by first screening for epistatic regions and then using dense markers within the identified regions, resulting in substantial reduction in the number of tests for epistasis. The FDR is controlled using a hierarchical procedure that accounts for the search structure. Each combination of trait and marker-pair is tested using a model that accounts for both statistical and functional interpretations of epistasis and considers orthogonal effects, such that their contributions to heritability can be estimated individually. We examine the impact of using multi-trait complexes rather than single traits, and of using a hierarchical search for epistasis rather than skipping the initial screen for epistatic regions. We apply the proposed algorithm on <i>Arabidopsis</i> transcription data.</p><p>Principal Findings</p><p>Both epistasis detection power and heritability contributed by epistasis increased when using multi-trait complexes rather than single traits. Epistatic effects common to the eQTLs included in the complexes have higher chance of being identified by analysis of multi-trait complexes, particularly when epistatic effects on individual traits are small. Compared to direct testing for all potential epistatic effects, the hierarchical search was substantially more powerful in detecting epistasis, while controlling the FDR at the desired level. Association in functional roles within genomic regions was observed, supporting an initial screen for epistatic QTLs.</p></div

Q–Q-plot of –log(p-values) in single-trait analysis, assuming exponential distribution under the null.

<p>The p-values are for significance of epistatic interaction in the NOIA model. As expected, the p-values corresponding the null cases (marked by diamonds) follow a straight line that represents their distribution under the null. The p-values corresponding the presumably weak epistatic effects on meta-traits (marked by squares) generate p-values that are distinctly lower than the null p-values. The p-values corresponding the presumably strong epistatic effects on single-traits (marked by triangles) generate the lowest p-values.</p

Power comparison between QTLBIM and the hierarchical testing approach.

<p>Simulation results. Standard error is <0.02 for power, <0.0007 for single traits and <0.0009 for meta-traits.</p><p>Power comparison between QTLBIM and the hierarchical testing approach.</p

Arabidopsis genome - detected epistasis effects.

<p>Arabidopsis genome - detected epistasis effects.</p

FDR comparison between QTLBIM and the hierarchical testing approach for meta-traits analysis – simulation results.

<p>FDR, estimated by the average proportion of erroneously identified epistatic effects among all identified epistatic effects, is plotted against the epistatic effect size. The solid lines mark the FDR obtained by the QTLBIM method, and the dashed lines mark the FDR obtained by the proposed hierarchical approach. Standard error was <0.025 for single traits and <0.003 for meta-traits.</p

A schematic description of the two-stage epistasis detection algorithm.

<p>Meta-traits with the corresponding traits are presented as ellipses with dots. Below them a partial genetic map of five <i>Arabidopsis</i> chromosomes is zoomed in. Filled circles and short vertical lines denote “framework” and “secondary” markers, respectively, and long vertical lines denote borders of sparse marker regions. The figure describes discovery of epistasis for the combination of meta-trait <i>g</i>, sparse marker pair and in the first step, and their “secondary” markers (in grey) in the second step.</p

Epistasis detection power – simulation results.

<p>Power, estimated by the proportion of epistatic effects detected by the procedure, is plotted against the epistatic effect size. Standard error was <0.003 for single traits and <0.025 for meta-traits. (a) Power levels are saturated for the multi-trait complexes analysis on models with main effects, and thus lower epistatic effects are used in Fig. 3b. (b) Reduced sizes of epistatic effects for the multi-trait complexes analysis, which generate non-saturated power levels, are examined on models with main effects. b. Epistasis detection power for the meta-traits analysis on models with main effects - simulation results. Power, estimated by the proportion of epistatic effects detected by the procedure, is plotted against the epistatic effect size. Standard error was <0.003 for single traits and <0.025 for meta-traits.</p

Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients

Abstract Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment