Computational protocol: Modest familial risks for multiple sclerosis: a registry-based study of the population of Sweden

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Protocol publication

[…] The cumulative age at onset distribution was estimated for the Swedish Multiple Sclerosis Registry and the full data set. Crude and age-adjusted risks were calculated using Strömgrens unmodified method () for the latter, with the age at onset distribution from the Swedish Multiple Sclerosis Registry used to obtain the previous distribution.For the relative risks analyses, we constructed a data set with up to 10 randomly selected control pairs per case. Multiple sclerosis pairs for whom no suitable matched controls were available were excluded from the risk ratio analyses. The controls were matched on year of birth and sex, and their relatives were matched on the multiple sclerosis patient’s relative’s year of birth, sex and, where applicable, maternal/paternal relation to the index patient. Any control that had died before reaching the age of the multiple sclerosis index patient’s age of onset were excluded from the analysis, as were offspring adopted away. Index patients were included once for every relation investigated, and could thus occur more than once in the analyses. A Cox proportional hazards model [‘coxph’ function from the ‘survival’ package (; ) in R ()] with a robust sandwich estimator was used to estimate risk ratios and 95% confidence intervals (CI). Included in this model were sex and year of birth for the control(s), and age at onset, sex, year of birth, and if matched on maternal/paternal relation for the patient with multiple sclerosis. For confidence intervals, the robust standard error was used. To correct for multiple testing, the Bonferroni method was applied using a factor of 76. The PROC FREQ statement in the SAS software version 9.2 was used to estimate tetrachoric correlations and confidence intervals were calculated using the estimated asymptotic standard error.Twins and their zygosity were identified through the Swedish Twin Registry. An analysis of the heritability was made using OpenMx () in R. In OpenMx, twin pairs are used to estimate the variation within a trait, which is then explained by three parameters. ‘A’, more commonly referred to as h2, denotes the genetic part of the contribution to disease. ‘C’ is the shared environmental component within a family, and ‘E’ is the non-shared environmental component (). Sex was included in the model as a covariate. To increase power, from every family in the Multi Generation Registry the two oldest siblings and half-siblings with no more than 5 years of age difference were included in the analysis. All siblings adopted or adopted away were excluded.Testing for a possible increase in transmission from the lower prevalent sex to offspring, also known as the Carter effect, was conducted with Pearson’s chi-squared test by assessing the differences in transmission rates between maternal and paternal parent to children using the stats package in R. Confidence intervals for the odds ratios (OR) were calculated with Fisher’s conditional maximum likelihood estimation in R using the ‘oddsratio’ function from the ‘epitools’ package (). […]

Pipeline specifications

Software tools OpenMx, EpiTools
Application Miscellaneous
Organisms Homo sapiens
Diseases Multiple Sclerosis