Common variants near MC4R are associated with fat mass, weight and risk of obesity.

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits

No News Is Good News: Stochastic Parameters versus Media Coverage Indices in Demand Models after Food Scares

We develop a stochastic parameter approach to model the time-varying impacts of food scares on consumption, as an alternative to the inclusion of news coverage indices in the demand function. We empirically test the methodology on data from four food scares, the 1982 heptachlor milk contamination in Oahu, Hawaii and the BSE and two E-coli scares on US meat demand over the period 1993-1999. Results show that the inclusion of time-varying parameters in demand models enables the capturing of the impact of food safety information and provides better short-term forecasts