Computational protocol: Genetic Factors Influence the Clustering of Depression among Individuals with Lower Socioeconomic Status

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[…] General characteristics were compared between men and women and tested using ANOVA for continuous variables and chi-squared test for dichotomous variables. To quantify the strength of the phenotypic association between symptoms of depression and education, partial correlations (ρ) were calculated. Associations were explored by univariate and multivariate linear regression (SPSS version 11.0 for Windows; SPSS, Chicago, IL). All determinants below the 0.10 significance level in the multivariate analyses were retained in the final model for heritability estimation. Multiple linear regression models were fitted to examine the association of covariates with symptoms of depression and to assess the distributional assumption of normality. The normality of residuals was tested using a one-sample Kolmogorov-Smirnov test. SPSS 11.0 for Windows was used.A full pedigree variance components approach based on maximum-likelihood methods was used to estimate the heritability of symptoms of depression and of education. Univariate quantitative genetic analysis was performed to partition the phenotypic variance of symptoms of depression variables into additive genetic and environmental variance components using maximum-likelihood variance decomposition methods., The phenotypic variance of the variables, which reflects the inter-individual variation, was partitioned into its additive genetic (σ2G) and residual environmental (σ2E) variance components. The environmental variance is the mean residual, unexplained variance, which is not explained by the factors measured in the analysis (i.e. additive genetic factors or covariates). With genetic variance we mean the additive genetic component of the variance.Heritability was estimated as the ratio of the additive genetic variance to the sum of the additive genetic and environmental variance, that is including sources of residual variance as measurement error: h2 = (additive) σ2G/(σ2G+σ2E). Dominance variance, which, in conjunction with additive and environmental variance, comprises broad sense heritability, was not estimated. Dominance effects are more easily modeled in twin than in family studies but they are difficult to model in extended pedigrees, we assumed additive effects.Bivariate analyses were performed to estimate the genetic and environmental correlations between the symptoms of depression and education., The genetic and environmental correlations can be calculated from the phenotypic correlations (ρP) by the following formula ρP = [square root]h12[square root]h22ρG+[square root](1−h12)[square root](1−h22)ρE,, where h12 and h22 are the heritability estimates of the traits for which the phenotypic correlation is calculated, and ρG and ρE are the genetic and environmental correlations between these two traits. Significance of the phenotypic, additive genetic and environmental correlations was determined using a likelihood ratio test. To test whether a given correlation between two traits was significantly different from zero, the likelihood of a model in which this correlation was constrained to zero was compared with a model in which the same correlation was estimated. Twice the difference in ln-likelihoods of these models yields a test statistic that is asymptotically distributed as a chi-squared statistic with degrees of freedom equal to the difference in number of parameters estimated in the two models.Analyses were adjusted for age, sex, use of medication, degree of consanguinity and sibship effects. The degree of consanguinity, indicating the degree to which parents of each participant are related to each other through their ancestors, was estimated using the Fortran software Package for Pedigree Analysis (PEDIG), based on the pedigree of the total population. PEDIG yielded a coefficient for each participant, which was then entered as a covariate in the calculation of the heritability and genetic correlations. Sibship effects denote the exposure to early environmental factors that are shared by children of the same household. In this study, sibship effect estimates were phenotypic similarities induced in the progeny of the same mother. This effect is a combination of effects induced by shared early life environment and dominant genetic effects. Because of the small number of half sibs in our sample and the non-delineation of household effects in our data set, the effect due to sharing the same mother is almost indistinguishable from the sibship effect.Finally, to investigate the extent to which neuroticism and intelligence were intermediate factors in the causal pathway between the shared genetic factors and the co-occurrence of symptoms of depression and lower socioeconomic status, the analyses were additionally adjusted for NEO-FFI neuroticism scores and DART premorbid intelligence scores. In this analysis we assume that if neuroticism and intelligence are intermediate factors in the pathway, (genetic) correlations will disappear when adjusting for these factors. SOLAR (Sequential Oligogenic Linkage Analysis Routines) 2.1.2 software package (Southwest Foundation for Biomedical Research, San Antonio, Texas, USA) was used for the calculation of heritability estimates and for the genetic and environmental correlations. P values lower than 0.05 (two-tailed) were considered statistically significant. […]

Pipeline specifications

Software tools PEDIG, SOLAR
Application Population genetic analysis
Organisms Homo sapiens