Design and Implementation of a Computational Platform and a Parallelized Interaction Analysis for Large Scale Genomics Data in Multiple Sclerosis

Abstract The multiple sclerosis (MS) genetics research group led by professor Jan Hillert at Karolinska Institutet, focuses on investigating the aetiology of the disease. Samples have been collected routinely from patients visiting the clinic for decades. From these samples, large amounts of genetics data is being generated. The traditional methods of analyzing the data is becoming increasingly inefficient as data sets grow larger. New approaches are needed to perform the analyses. This thesis gives an introduction to the relevant genetics and discusses possible approaches for enabling more efficient execution of legacy analysis tools, as well as improving a gene-environment and gene-gene interaction analysis. Different computational paradigms are presented followed by the implementation of a computational platform to support the researchers' existing, and possibly future, analysis needs. The improved interaction analysis application is then implemented and executed in a virtual instance of this platform. The performance of the analysis application is then evaluated with respect to the original reference application. Referat Design och implementation av beräkningsplattform och paralelliserad interaktionsanalys för storskaliga genetiska data inom multipel skleros Professor Jan Hillert vid Karolinska Institutet leder en forskargrupp som fokuserar på etiologin bakom multipel skleros (MS). Under flera årtionden har patientprover samlats in från kliniken och från dessa prover har stora mängder genetiska data genererats. De traditionella analysmetoderna blir allt mer ineffektiva då datamängderna öker. Det finns ett stort behov av nya tillvägagångssätt och metoder för att analysera dessa data. Denna uppsats ger en introduktion i relevant genetik och diskuterar olika tillvägagångssätt för att möjliggöra effektivare exekvering av befintliga analysverktyg, så väl som förbättring av en gen-miljö och gen-gen-interaktionsanalys. Olika etablerade beräkningsparadigmer presenteras, följt av en implementation av en beräkningsplattform som ett stöd i att tillgodose forskargruppens nuvarande och möjli-ga framtida behov. Den förbättrade interaktionsanalysen är sedan implementerad och exekverad i en virtuell instans av plattformen. Interaktionsanalysens prestanda utvärderas sedan och jämförs med ursprungsimplementationen

The Genetic Interacting Landscape of 63 Candidate Genes in Major Depressive Disorder: An Explorative Study

Background: Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Results: Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model Puncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with Puncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (\u3c 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (\u3c 0.15). Conclusions: We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied

Design och implementation av beräkningsplattform och paralelliserad interaktionsanalys för storskaliga genetiska data inom multipel skleros

The multiple sclerosis (MS) genetics research group led by professor Jan Hillert at Karolinska Institutet, focuses on investigating the aetiology of the disease. Samples have been collected routinely from patients visiting the clinic for decades. From these samples, large amounts of genetics data is being generated. The traditional methods of analyzing the data is becoming increasingly inefficient as data sets grow larger. New approaches are needed to perform the analyses. This thesis gives an introduction to the relevant genetics and discusses possible approaches for enabling more efficient execution of legacy analysis tools, as well as improving a gene-environment and gene-gene interaction analysis. Different computational paradigms are presented followed by the implementation of a computational platform to support the researchers’ existing, and possibly future, analysis needs. The improved interaction analysis application is then implemented and executed in a virtual instance of this platform. The performance of the analysis application is then evaluated with respect to the original reference application.Professor Jan Hillert vid Karolinska Institutet leder en forskargrupp som fokuserar på etiologin bakom multipel skleros (MS). Under flera årtionden har patientprover samlats in från kliniken och från dessa prover har stora mängder genetiska data genererats. De traditionella analysmetoderna blir allt mer ineffektiva då datamängderna öker. Det finns ett stort behov av nya tillvägagångssätt och metoder för att analysera dessa data. Denna uppsats ger en introduktion i relevant genetik och diskuterar olika tillvägagångssätt för att möjliggöra effektivare exekvering av befintliga analysverktyg, så väl som förbättring av en gen-miljö och gen-gen-interaktionsanalys. Olika etablerade beräkningsparadigmer presenteras, följt av en implementation av en beräkningsplattform som ett stöd i att tillgodose forskargruppens nuvarande och möjliga framtida behov. Den förbättrade interaktionsanalysen är sedan implementerad och exekverad i en virtuell instans av plattformen. Interaktionsanalysens prestanda utvärderas sedan och jämförs med ursprungsimplementationen