Concerning the relationships between genes, risk factors and immunity in Alzheimer's disease, Autism, Bipolar disorder , multiple sclerosis, Parkinson's disease, schizophrenia and chronic fatigue
Using Summary Data from the Danish National Registers to Estimate Heritabilities for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.
Estimates of heritability of psychiatric disorders quantify the genetic contribution to their etiology. Estimation of these parameters requires affected status on probands and their family members. Traditionally, heritabilities have been estimated from families ascertained from specific hospital registers, but accumulating sufficient numbers of families can be difficult. Larger sample sizes are achievable from national registries, but calculation of heritability from individual level data from these data sets is accompanied by other problems. Here, we use published summary data from a national population-based cohort of >2.6 million persons in Denmark to estimate heritabilities of schizophrenia, bipolar disorder, and major depressive disorder (MDD). The summary data comprised cumulative incidences up to 52 years of age for schizophrenia and bipolar disorder and up to 51 years for MDD in offspring where either one or both parents were diagnosed with one of these disorders. Estimates of the heritabilities of the liability to developing schizophrenia, bipolar disorder, and MDD are 0.67 (95% confidence interval (CI) 0.64-0.71), 0.62 (95% CI 0.58-0.65), and 0.32 (95% CI 0.30-0.34) respectively. The estimates may be inflated by common environmental effects, but despite this, they are somewhat lower for schizophrenia and bipolar disorder than those estimated from contemporary twin samples. The lower estimates may reflect the diverse environments (including diagnostic interpretation) that contribute to national data, compared to twin/family studies. Our estimates are similar to those estimated previously from national data of Sweden, and they may be more representative of the international samples brought together for large-scale genome-wide association studies. We investigated the estimation of genetic correlations from these data. We used simulation to conclude that estimates may not be interpretable and so report them only in the Section "Appendix."
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