Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

Schizophrenia (SCZ) is a severe mental disorder with a lifetime risk of about 1%, characterized by hallucinations, delusions and cognitive deficits with heritability estimated at up to 80%1,2. We adopted two analytic approaches to determine the extent to which common genetic variation underlies risk of SCZ using genome-wide association study (GWAS) data from 3,322 European individuals with SCZ and 3,587 controls. First, we implicate the major histocompatibility complex (MHC). Second, we provide molecular genetic evidence for a substantial polygenic component to risk of SCZ involving thousands of common alleles of very small effect. We show that this component also contributes to risk of bipolar disorder (BPD), but not to multiple non-psychiatric diseases.

Activation of melanocortin-4-receptors (MC4Rs) reduces body fat stores by decreasing food intake and increasing energy expenditure. MC4Rs are expressed in multiple CNS sites, any number of which could mediate these effects. To identify the functionally relevant sites of MC4R expression, we generated a loxP-modified, null Mc4r allele (loxTB Mc4r) that can be reactivated by Cre-recombinase. Mice homozygous for the loxTB Mc4r allele do not express MC4Rs and are markedly obese. Restoration of MC4R expression in the paraventricular hypothalamus (PVH) and a subpopulation of amygdala neurons, using Sim1-Cre transgenic mice, prevented 60% of the obesity. Of note, increased food intake, typical of Mc4r null mice, was completely rescued while reduced energy expenditure was unaffected. These findings demonstrate that MC4Rs in the PVH and/or the amygdala control food intake but that MC4Rs elsewhere control energy expenditure. Disassociation of food intake and energy expenditure reveals unexpected divergence in melanocortin pathways controlling energy balance.