Abstract
Analysis of raw pooled data from distinct studies of a single question generates a single statistical conclusion with greater power and precision than conventional metaanalysis based on within-study estimates. However, conducting analyses with pooled genetic data, in particular, is a daunting task that raises important statistical issues. In the process of analyzing data pooled from nine studies on the human leptin receptor (LEPR) gene for the association of three alleles (K109R, Q223R, and K656N) of LEPR with body mass index (BMI; kilograms divided by the square of the height in meters) and waist circumference (WC), we encountered the following methodological challenges: data on relatives, missing data, multivariate analysis, multiallele analysis at multiple loci, heterogeneity, and epistasis. We propose herein statistical methods and procedures to deal with such issues. With a total of 3263 related and unrelated subjects from diverse ethnic backgrounds such as African-American, Caucasian, Danish, Finnish, French-Canadian, and Nigerian, we tested effects of individual alleles; joint effects of alleles at multiple loci; epistatic effects among alleles at different loci; effect modification by age, sex, diabetes, and ethnicity; and pleiotropic genotype effects on BMI and WC. The statistical methodologies were applied, before and after multiple imputation of missing observations, to pooled data as well as to individual data sets for estimates from each study, the latter leading to a metaanalysis. The results from the metaanalysis and the pooling analysis showed that none of the effects were significant at the 0.05 level of significance. Heterogeneity tests showed that the variations of the nonsignificant effects are within the range of sampling variation. Although certain genotypic effects could be population specific, there was no statistically compelling evidence that any of the three LEPR alleles is associated with BMI or waist circumference in the general population.