Background A persistent debate in psychiatry concerns whether schizophrenia and bipolar disorder are the clinical realizations of discrete versus shared etiological processes. Methods We linked the Multi-Generation Register, containing information about children and their parents of all Swedes, and the Hospital Discharge Register, covering all public psychiatric inpatient hospitalizations in Sweden. We identified 9,009,202 unique individuals in more than 2 million nuclear families. Risks for schizophrenia, bipolar disorder and their co-morbidity were calculated for biological and adoptive parents, offspring, full siblings and half-siblings of probands with the diseases. A multivariate generalized linear mixed model was used to estimate genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and their co-morbidity. Findings There were increased risks of both schizophrenia and bipolar disorder to first degree relatives of probands with either disorder. Half-sibs had a significantly increased risk, albeit substantially lower than the full-siblings. When relatives of probands with bipolar disorder were analysed, increased risks for schizophrenia were present for all relationships, including offspring adopted away. Heritability for schizophrenia was 64% and for bipolar disorder 59%. Shared environmental effects were small but significant for both disorders. The co-morbidity between the disorders was primarily (63%) due to additive genetic effects common to both disorders. Interpretation Similar to molecular genetic studies, we found compelling evidence that schizophrenia and bipolar disorder partially share a common genetic etiology. These results challenge the current nosological dichotomy between schizophrenia and bipolar disorder, and are consistent with a reappraisal of these disorders as distinct diagnostic entities.

Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.

A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (henceforth autism) the nature of its allelic spectrum is uncertain. Individual risk genes have been identified from rare variation, especially de novo mutations1–8. From this evidence one might conclude that rare variation dominates its allelic spectrum, yet recent studies show that common variation, individually of small effect, has substantial impact en masse9,10. At issue is how much of an impact relative to rare variation. Using a unique epidemiological sample from Sweden, novel methods that distinguish total narrow-sense heritability from that due to common variation, and by synthesizing results from other studies, we reach several conclusions about autism’s genetic architecture: its narrow-sense heritability is ≈54% and most traces to common variation; rare de novo mutations contribute substantially to individuals’ liability; still their contribution to variance in liability, 2.6%, is modest compared to heritable variation.

Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low-and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.