Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools.

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine's polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100-12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of 'pleiotropic' variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation.

Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder.

Bipolar disorder is a complex neuropsychiatric disorder that is characterized by intermittent episodes of mania and depression; without treatment, 15% of patients commit suicide. Hence, it has been ranked by the World Health Organization as a top disorder of morbidity and lost productivity. Previous neuropathological studies have revealed a series of alterations in the brains of patients with bipolar disorder or animal models, such as reduced glial cell number in the prefrontal cortex of patients, upregulated activities of the protein kinase A and C pathways and changes in neurotransmission. However, the roles and causation of these changes in bipolar disorder have been too complex to exactly determine the pathology of the disease. Furthermore, although some patients show remarkable improvement with lithium treatment for yet unknown reasons, others are refractory to lithium treatment. Therefore, developing an accurate and powerful biological model for bipolar disorder has been a challenge. The introduction of induced pluripotent stem-cell (iPSC) technology has provided a new approach. Here we have developed an iPSC model for human bipolar disorder and investigated the cellular phenotypes of hippocampal dentate gyrus-like neurons derived from iPSCs of patients with bipolar disorder. Guided by RNA sequencing expression profiling, we have detected mitochondrial abnormalities in young neurons from patients with bipolar disorder by using mitochondrial assays; in addition, using both patch-clamp recording and somatic Ca(2+) imaging, we have observed hyperactive action-potential firing. This hyperexcitability phenotype of young neurons in bipolar disorder was selectively reversed by lithium treatment only in neurons derived from patients who also responded to lithium treatment. Therefore, hyperexcitability is one early endophenotype of bipolar disorder, and our model of iPSCs in this disease might be useful in developing new therapies and drugs aimed at its clinical treatment.