title: The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.
abstract: 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 (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 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.
authors.affiliation: Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Magnus
authors.initials: M
authors.lastname: Lekman
authors.affiliation: Department of Mathematics, Stockholm University, Stockholm, Sweden.
authors.forename: Ola
authors.initials: O
authors.lastname: Hössjer
authors.affiliation: Institute of Quantitative Biomedical Science, Department of Genetics and Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Andrews
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Henrik
authors.initials: H
authors.lastname: Källberg
authors.affiliation: Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Daniel
authors.initials: D
authors.lastname: Uvehag
authors.affiliation: Department of Psychiatry, Mount Sinai School of Medicine, New York, NY, USA.
authors.forename: Dennis
authors.initials: D
authors.lastname: Charney
authors.affiliation: Laboratory of Molecular Pathophysiology, NIMH, NIH, Department of Health & Human Services (DHHS), Bethesda, USA.
authors.forename: Husseini
authors.initials: H
authors.lastname: Manji
authors.affiliation: Academic Medical Research Institute, Duke-National University of Singapore, Singapore, Singapore.
authors.forename: John A
authors.initials: JA
authors.lastname: Rush
authors.affiliation: Genetic Basis of Mood & Anxiety Disorders Section, Human Genetic Branch, NIMH, NIH, Department of Health & Human Services (DHHS), Bethesda, USA.
authors.forename: Francis J
authors.initials: FJ
authors.lastname: McMahon
authors.affiliation: Institute of Quantitative Biomedical Science, Department of Genetics and Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Ingrid
authors.initials: I
authors.lastname: Kockum
keywords: Additive interaction; Data mining and machine learning; Logistic regression; Major depressive disorder; Multiplicative interaction