It has been claimed and demonstrated that many (and possibly most) of the conclusions drawn from biomedi-cal research are probably false 1. A central cause for this important problem is that researchers must publish in order to succeed, and publishing is a highly competitive enterprise, with certain kinds of findings more likely to be published than others. Research that produces novel results, statistically significant results (that is, typically p < 0.05) and seemingly 'clean' results is more likely to be published 2,3. As a consequence, researchers have strong incentives to engage in research practices that make their findings publishable quickly, even if those practices reduce the likelihood that the findings reflect a true (that is, non-null) effect 4. Such practices include using flexible study designs and flexible statistical analyses and running small studies with low statistical power 1,5. A simulation of genetic association studies showed that a typical dataset would generate at least one false positive result almost 97% of the time 6 , and two efforts to replicate promising findings in biomedicine reveal replication rates of 25% or less 7,8. Given that these publishing biases are pervasive across scientific practice, it is possible that false positives heavily contaminate the neuroscience literature as well, and this problem may affect at least as much, if not even more so, the most prominent journals 9,10. Here, we focus on one major aspect of the problem: low statistical power. The relationship between study power and the veracity of the resulting finding is under-appreciated. Low statistical power (because of low sample size of studies, small effects or both) negatively affects the likelihood that a nominally statistically significant finding actually reflects a true effect. We discuss the problems that arise when low-powered research designs are pervasive. In general, these problems can be divided into two categories. The first concerns problems that are mathematically expected to arise even if the research conducted is otherwise perfect: in other words, when there are no biases that tend to create statistically significant (that is, 'positive') results that are spurious. The second category concerns problems that reflect biases that tend to co-occur with studies of low power or that become worse in small, underpowered studies. We next empirically show that statistical power is typically low in the field of neuroscience by using evidence from a range of subfields within the neuroscience literature. We illustrate that low statistical power is an endemic problem in neuroscience and discuss the implications of this for interpreting the results of individual studies. Low power in the absence of other biases Three main problems contribute to producing unreliable findings in studies with low power, even when all other research practices are ideal. They are: the low probability of finding true effects; the low positive predictive value (PPV; see BOX 1 for definitions of key statistical terms) when an eff...

We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

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Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Identification of the genes underlying complex phenotypes and the definition of the evolutionary forces that have shaped eukaryotic genomes are among the current challenges in molecular genetics. Variation in gene copy number is increasingly recognized as a source of inter-individual differences in genome sequence and has been proposed as a driving force for genome evolution and phenotypic variation. Here we show that copy number variation of the orthologous rat and human Fcgr3 genes is a determinant of susceptibility to immunologically mediated glomerulonephritis. Positional cloning identified loss of the newly described, rat-specific Fcgr3 paralogue, Fcgr3-related sequence (Fcgr3-rs), as a determinant of macrophage overactivity and glomerulonephritis in Wistar Kyoto rats. In humans, low copy number of FCGR3B, an orthologue of rat Fcgr3, was associated with glomerulonephritis in the autoimmune disease systemic lupus erythematosus. The finding that gene copy number polymorphism predisposes to immunologically mediated renal disease in two mammalian species provides direct evidence for the importance of genome plasticity in the evolution of genetically complex phenotypes, including susceptibility to common human disease.

CD8 + T lymphocytes play a key role in host defense, in particular against important persistent viruses, although the critical functional properties of such cells in tissue are not fully defined. We have previously observed that CD8 + T cells specific for tissue-localized viruses such as hepatitis C virus express high levels of the C-type lectin CD161. To explore the significance of this, we examined CD8 + CD161 + T cells in healthy donors and those with hepatitis C virus and defined a population of CD8 + T cells with distinct homing and functional properties. These cells express high levels of CD161 and a pattern of molecules consistent with type 17 differentiation, including cytokines (e.g., IL-17, IL-22), transcription factors (e.g., retinoic acid-related orphan receptor γ-t, P = 6 × 10 −9 ; RUNX2, P = 0.004), cytokine receptors (e.g., IL-23R, P = 2 × 10 −7 ; IL-18 receptor, P = 4 × 10 −6 ), and chemokine receptors (e.g., CCR6, P = 3 × 10 −8 ; CXCR6, P = 3 × 10 −7 ; CCR2, P = 4 × 10 −7 ). CD161 + CD8 + T cells were markedly enriched in tissue samples and coexpressed IL-17 with high levels of IFN-γ and/or IL-22. The levels of polyfunctional cells in tissue was most marked in those with mild disease ( P = 0.0006). These data define a T cell lineage that is present already in cord blood and represents as many as one in six circulating CD8 + T cells in normal humans and a substantial fraction of tissue-infiltrating CD8 + T cells in chronic inflammation. Such cells play a role in the pathogenesis of chronic hepatitis and arthritis and potentially in other infectious and inflammatory diseases of man.

SummaryMutations in whole organisms are powerful ways of interrogating gene function in a realistic context. We describe a program, the Sanger Institute Mouse Genetics Project, that provides a step toward the aim of knocking out all genes and screening each line for a broad range of traits. We found that hitherto unpublished genes were as likely to reveal phenotypes as known genes, suggesting that novel genes represent a rich resource for investigating the molecular basis of disease. We found many unexpected phenotypes detected only because we screened for them, emphasizing the value of screening all mutants for a wide range of traits. Haploinsufficiency and pleiotropy were both surprisingly common. Forty-two percent of genes were essential for viability, and these were less likely to have a paralog and more likely to contribute to a protein complex than other genes. Phenotypic data and more than 900 mutants are openly available for further analysis.PaperClip

Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide1, poses a major challenge to genetic analysis. To date no robustly replicated genetic loci have been identified 2, despite analysis of more than 9,000 cases3. Using low coverage genome sequence of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified and replicated two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P-value = 2.53×10−10) the other in an intron of the LHPP gene (P = 6.45×10−12). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.