DataSheet1_Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.docx

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.</p

Percent agreement by educational level for selected items.

<p>Percent agreement by educational level for selected items.</p

Descriptive measures of the sample.

<p>Descriptive measures of the sample.</p

Percent agreement by professional role in the healthcare system for selected items.

<p>Percent agreement by professional role in the healthcare system for selected items.</p

The wish to know in different diagnostic scenarios.

<p>The wish to know in different diagnostic scenarios.</p

Empirical and theoretical distributions of the total score in the Consortium on Lithium Genetics sample.

<p>Frequentist, <b>A</b>, and Bayesian minimum message length, <b>B</b>, mixture modeling identify three subpopulations of non responders (grey), partial responders (red), and full responders (blue) in total scores of 1,308 bipolar disorder patients characterized for response to lithium maintenance treatment.</p

Inter-rater agreement and reliability of the assessment of lithium response in the two-stage case-vignette rating procedure: kappa and intra-class correlation analysis.

<p>TS: total score.</p><p>ICC: intra-class correlation.</p><p>CI: confidence interval.</p>*<p>Mixed and random effects models.</p>§<p>70 raters.</p>¶<p>48 raters.</p

Number of raters from the Consortium on Lithium Genetics (ConLiGen) centres participating in the two-stage case-vignette rating procedure for inter-rater reliability and agreement.

<p>ConLiGen: Consortium on Lithium Genetics.</p>*<p>Hokkaido, Osaka, Tokio, Riken Brain Science Institute.</p

Distribution of total and A scores in the Consortium on Lithium Genetics sample.

<p>Histogram plot of the scale scores in 1,308 bipolar disorder patients characterized for response to lithium maintenance treatment.</p