Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism to date, including 7,824 adult individuals from two European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity regarding more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information regarding gene expression, heritability, overlap with known drug targets, previous association with complex disorders and inborn errors of metabolism. We further developed a database and web-based resources for data mining and results visualization. Our findings contribute to a greater understanding of the role of inherited variation in blood metabolic diversity, and identify potential new opportunities for pharmacologic development and disease understanding.

Stargazer, an ataxic and epileptic mutant mouse, lacks functional AMPA (alpha-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate) receptors on cerebellar granule cells. Stargazin, the mutated protein, interacts with both AMPA receptor subunits and synaptic PDZ proteins, such as PSD-95. The interaction of stargazin with AMPA receptor subunits is essential for delivering functional receptors to the surface membrane of granule cells, whereas its binding with PSD-95 and related PDZ proteins through a carboxy-terminal PDZ-binding domain is required for targeting the AMPA receptor to synapses. Expression of a mutant stargazin lacking the PDZ-binding domain in hippocampal pyramidal cells disrupts synaptic AMPA receptors, indicating that stargazin-like mechanisms for targeting AMPA receptors may be widespread in the central nervous system.

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It is widely but incorrectly believed that the t-test and linear regression are valid only for Normally distributed outcomes. The t-test and linear regression compare the mean of an outcome variable for different subjects. While these are valid even in very small samples if the outcome variable is Normally distributed, their major usefulness comes from the fact that in large samples they are valid for any distribution. We demonstrate this validity by simulation in extremely non-Normal data. We discuss situations in which in other methods such as the Wilcoxon rank sum test and ordinal logistic regression (proportional odds model) have been recommended, and conclude that the t-test and linear regression often provide a convenient and practical alternative. The major limitation on the t-test and linear regression for inference about associations is not a distributional one, but whether detecting and estimating a difference in the mean of the outcome answers the scientific question at hand.