Massively parallel reporter assays (MPRAs) have emerged as a popular means for understanding noncoding variation in a variety of conditions. While a large number of experiments have been described in the literature, analysis typically uses ad-hoc methods. There has been little attention to comparing performance of methods across datasets. We present the mpralm method which we show is calibrated and powerful, by analyzing its performance on multiple MPRA datasets. We show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets. We investigate theoretical and real-data properties of barcode summarization methods and show an unappreciated impact of summarization method for some datasets. Finally, we use our model to conduct a power analysis for this assay and show substantial improvements in power by performing up to 6 replicates per condition, whereas sequencing depth has smaller impact; we recommend to always use at least 4 replicates. An R package is available from the Bioconductor project. Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments.

Schizophrenia is a devastating mental illness with a strong genetic component that is the subject of extensive research. Despite the high heritability, it is well recognized that non-genetic factors such as certain infections, cannabis use, psychosocial stress, childhood adversity, urban environment, and immigrant status also play a role. Whenever genetic and non-genetic factors co-exist, interaction between the two is likely. This means that certain exposures would only be of consequence given a specific genetic makeup. Here, we provide a brief review of studies reporting evidence of such interactions, exploring genes and variants that moderate the effect of the environment to increase risk of developing psychosis. Discovering these interactions is crucial to our understanding of the pathogenesis of complex disorders. It can help in identifying individuals at high risk, in developing individualized treatments and prevention plans, and can influence clinical management.

Alzheimer's disease is a progressive neurodegenerative disorder with high prevalence in old age. It is the most common cause of dementia, with a risk reaching 50% after the age of 85 years, and with the increasing age of the population it is one of the biggest healthcare challenges of the 21st century. Genetic variation is an important contributor to the risk for this disease, underlying an estimated heritability of about 70%. Alzheimer's genetics research in the 1990s was successful in identifying three genes accounting for most cases of early-onset disease with autosomal dominant inheritance, and one gene involved in the more common late-onset disease, which shows complex inheritance patterns. Despite the presence of significant remaining genetic contribution to the risk, the identification of genes since then has been elusive, reminiscent of most other complex disorders. In the past decade there have been significant efforts towards a systematic evaluation of the multiple genetic association studies for Alzheimer's disease, while the first genome-wide association studies are now being reported with promising results. As sample sizes grow through new collections and collaborative efforts, and as new technologies make it possible to test alternative hypotheses, it is expected that new genes involved in the disease will soon be identified and confirmed. The gene discoveries of the 1990s have taught us a lot about Alzheimer's disease pathogenesis, providing many therapeutic targets that are currently at various stages of testing for future clinical use. As new genes become known and the biological pathways leading to disease are further explored, the possibility of prevention and successful personalized treatment is becoming tangible, providing hope for the millions of patients with Alzheimer's disease and their caregivers.

Effects of allele-specific open chromatin: Genetic variants in noncoding regions of the genome may underlie the development of disease. However, we are just beginning to tease apart the function of such variants associated with neuropsychiatric disease. Using five types of neural progenitor cells derived from 20 human induced pluripotent stem cell lines, Zhang et al. looked at allele-specific open chromatin (ASoC) variants. Many ASoC variants overlapped with genomic elements, such as transcription factor binding sites, and loci identified in genome-wide association studies for neurological traits. From the experimental and computational analyses, they identified single-nucleotide polymorphisms and illuminate how one schizophrenia-associated variant affects neurodevelopment. Science , this issue p. 561

Inflammation and maternal or fetal infections have been suggested as risk factors for schizophrenia (SZ) and bipolar disorder (BP). It is likely that such environmental effects are contingent on genetic background. Here, in a genome-wide approach, we test the hypothesis that such exposures increase the risk for SZ and BP and that the increase is dependent on genetic variants. We use genome-wide genotype data, plasma IgG antibody measurements against Toxoplasma gondii, Herpes simplex virus type 1, Cytomegalovirus, Human Herpes Virus 6 and the food antigen gliadin as well as measurements of C-reactive protein (CRP), a peripheral marker of inflammation. The subjects are SZ cases, BP cases, parents of cases and screened controls. We look for higher levels of our immunity/infection variables and interactions between them and common genetic variation genome-wide. We find many of the antibody measurements higher in both disorders. While individual tests do not withstand correction for multiple comparisons, the number of nominally significant tests and the comparisons showing the expected direction are in significant excess (permutation p=0.019 and 0.004 respectively). We also find CRP levels highly elevated in SZ, BP and the mothers of BP cases, in agreement with existing literature, but possibly confounded by our inability to correct for smoking or body mass index. In our genome-wide interaction analysis no signal reached genome-wide significance, yet many plausible candidate genes emerged. In a hypothesis driven test, we found multiple interactions among SZ-associated SNPs in the HLA region on chromosome 6 and replicated an interaction between CMV infection and genotypes near the CTNNA3 gene reported by a recent GWAS. Our results support that inflammatory processes and infection may modify the risk for psychosis and suggest that the genotype at SZ-associated HLA loci modifies the effect of these variables on the risk to develop SZ.