Genome-wide association studies (GWAS) have been successful in identifying thousands of trait and disease-associated single nucleotide polymorphisms (SNPs). The primary result of a GWAS analysis is a list of SNPs, their associated chromosomal position, and a Pvalue representing the statistical significance of the association. A commonly used method used to visualize GWAS results is the "manhattan plot"-a plot of the −log 10 (P) of the association statistic on the y-axis versus the chromosomal position of the SNP on the x-axis. Another commonly used results diagnostic plot is the quantile-quantile ("Q-Q") plot. Q-Q plots display the observed association P-value for all SNPs on the y-axis versus the expected uniform distribution of P-values under the null hypothesis of no association on the x-axis.

Immune dysfunction is commonly associated with several neurological and mental disorders. Although the mechanisms by which peripheral immunity may influence neuronal function are largely unknown, recent findings implicate meningeal immunity influencing behavior, such as spatial learning and memory1. Here we show that meningeal immunity is also critical for social behavior; mice deficient in adaptive immunity exhibit social deficits and hyper-connectivity of fronto-cortical brain regions. Associations between rodent transcriptomes from brain and cellular transcriptomes in response to T cell–derived cytokines suggest a strong interaction between social behavior and interferon-gamma (IFN-γ) driven responses. Concordantly, we demonstrate that inhibitory neurons respond to IFN-γ and increase GABAergic currents in projection neurons, suggesting that IFN-γ is a molecular link between meningeal immunity and neural circuits recruited for social behavior. Meta-analysis on the transcriptomes of a range of organisms revealed that rodents, fish, and flies elevate IFN-γ/JAK-STAT–dependent gene signatures in a social context, suggesting that the IFN-γ signaling pathway could mediate a co-evolutionary link between social/aggregation behavior and an efficient anti-pathogen response. This study implicates adaptive immune dysfunction, in particular IFN-γ, in disorders characterized by social dysfunction and suggests a co-evolutionary link between social behavior and an anti-pathogen immune response driven by IFN-γ signaling.

Summary: Genome-wide association studies (GWAS) have identified thousands of human trait-associated single nucleotide polymorphisms. Here, I describe a freely available R package for visualizing GWAS results using Q-Q and manhattan plots. The qqman package enables the flexible creation of manhattan plots, both genome-wide and for single chromosomes, with optional highlighting of SNPs of interest. Availability: qqman is released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=qqman). The source code is available on GitHub (https://github.com/stephenturner/qqman).

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of complex disease. The recent application of GWAS to clinic-based cohorts has also yielded genetic predictors of clinical outcomes. Regardless of context, the practical utility of this information will NIH Public Access Author ManuscriptCurr Protoc Hum Genet. Author manuscript; available in PMC 2012 January 1. ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. With each new dataset, new realities are discovered about GWAS data and best practices continue to be developed. The Genomics Workgroup of the National Human Genome Research Institute (NHGRI) funded electronic Medical Records and Genomics (eMERGE) network has invested considerable effort in developing strategies for QC of these data. The lessons learned by this group will be valuable for other investigators dealing with large scale genomic datasets. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the eMERGE network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. In this protocol we discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research.