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Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.
We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.
We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).
Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
This chapter reviews evidence supporting the hypothesis that genetic inheritance plays a substantial role in dependence on cocaine and (to a less well-studied degree) other illicit psychostimulants. The role of genes in cocaine dependence, however, may largely reflect a more general liability to develop dependence on a variety of substances. Studies of molecular genetic mechanisms in cocaine dependence remain in an early stage of development. A genome-wide association study (GWAS) of methamphetamine dependence, while yielding some interesting leads, requires replication in light of its small size, and reliance on pooled genotyping. While several intriguing candidate-gene associations between specific loci and cocaine dependence have been reported, to date there has yet to be a definitively replicated result reported. Clearly, more work is required in the human genetics of stimulant dependence, to identify and characterize how specific genes influence risk for this set of disorders.
Age of onset was examined for 139 members of 30 families affected by early-onset AD. Most (77%) of the variance of age of onset derived from differences between rather than within families. The constancy of age of onset within families was also observed in an analysis restricted to families derived from a population-based epidemiological study with complete ascertainment of early-onset AD. Furthermore, we observed clustering of age of onset within those families that support linkage to the predisposing locus on chromosome 21. Our data are compatible with the view that allelic heterogeneity at the AD locus may account for the similarity in age of onset within families. This finding may be of value for scientific studies of AD as well as for genetic counselling.
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