The mapping of complex/common traits is an important yet difficult research area. Many common human disorders are believed to be "complex" or multifactorial, meaning that they cannot be attributed to alleles of a single gene or one risk factor. Many genes and environmental factors contribute modest effects to a combined action in deciding these traits. Even the mutation spectrum for many simple Mendelian diseases can be quite complex. Mapping methodologies that simultaneously inspect disjoint loci are crucial for the success of genes mappings. We first review an alternative method proposed in (Lo and Zheng 2002, 2004), using family-trio data and several disease models, followed by a demonstration of applying this method to IBD data. The outcomes of this practice suggested the potentials of this approach in drawing substantial joint information in dealing with high dimensional problems. The main idea applies more generally than to special genetic problems. If time permits, a general version will be presented- dealing with the problem of detecting which, of many variables, have an effect on a dependent variable. In a sense, we are addressing the problem of locating a few needles in a haystack.