The metabolic syndrome and its components are a major health concern, particularly in Indian Asians. The GWAS approach has met with some success in dyslipidemia, type 2 diabetes and obesity phenotypes 
, with most studies to date being conducted in Europeans. Our study has further confirmed a number of previously reported associations, in some cases for the first time in Indian Asians, and identified some novel suggestive associations requiring further confirmation.
The metabolic syndrome consists of a number of phenotypes that tend to co-occur, raising the question of whether or not they have common genetic mechanisms 
. A number of definitions for the metabolic syndrome have been developed over the years, including those proposed by IDF, NCEP ATPIII or WHO 
. We chose the IDF definition, which is the most recent and incorporates ethnicity by providing different criteria for the metabolic syndrome in different ethnic groups 
. Most published associations for the metabolic syndrome are only with individual component phenotypes, or in some cases with multiple phenotypes but not matching any of the above definitions. Joy et al. 
reviewed a large number of genetic association and linkage studies for the metabolic syndrome using all definitions, and concluded that these studies have not provided any confirmed associations. Our results for Indian Asians also found no evidence for common genetic mechanisms underlying the metabolic syndrome, despite its high prevalence in this population, and the fact that we enriched for extreme metabolic syndrome phenotypes through selective genotyping of the participants.
Bayesian statistical methods are still rarely used in reporting genetic association studies, and we hope that our report will give further illustration of their potential benefits. Firstly, intuition gives little guide as to how important is an association of, say 8×10−7
, because the answer depends on power which in turn depends on several factors including the MAF. In contrast, a PPA of 0.6 is immediately interpretable irrespective of power or any multiple testing issues. On the other hand, p-values are usually simpler to compute, compared with a PPA that requires assumptions about the distribution of effect sizes 
, and so we have reported both here. We required that associations satisfy both p<10−6
and PPA>0.2 to be worthy of reporting in (weaker suggestive associations are reported in Table S1
). A second advantage of a Bayesian approach is that it has allowed us to give some weight to non-additive genetic associations, while still giving an appropriate weight to the more prevalent additive associations. Using p-values it is not easy for the researcher to control the weight given to different genetic models in an interpretable way. In fact, the associations reported in are all consistent with an additive genetic model, and allowing for general associations had little impact on our results. However we were able to allow for this possibility without incurring a multiple testing penalty.