Human pedigrees are useful not only for locating disease genes, through linkage analysis, but also for verifying relationships among individuals, detection of genotyping errors and for identification of haplotypes. We propose to build tools and methods for much faster, memory-efficient pedigree analysis that will enable geneticists to analyze thousands of single-nucleotide polymorphism (SNP) markers and larger family datasets. Our preliminary investigations in this area have already enabled geneticists to tackle larger problems than was previously feasible. For example, Merlin, a program we developed, allowed investigators at the Sanger Center and the University of Oxford to characterize chromosome wide haplotypes for 1504 chromosome 22 SNP markers in 7 pedigrees. Specific aims for this project include further improvements in the estimation of haplotypes in pedigrees, superior detection and modeling of genotype error, and a comprehensive simulation framework for evaluating linkage findings in situations where multiple phenotypes are considered. We propose to allow for differences between male-and-female recombination rates and for X-chromosome data in our methods and software. The advances we propose will be especially useful in projects that seek to identify the genetic basis of complex disease, such as cardiovascular disease, diabetes, obesity and asthma. These diseases are often described by multiple non-independent phenotypes, so that findings must be evaluated through computationally demanding simulation, and are also the most likely to rely on high-throughput SNP genotyping for fine mapping. Our tools will also be important for projects currently underway that seek to identify and catalog common haplotypes in the human genome. The advances we propose will turn many of the analyses that are now challenging into routine and allow investigators to extract the benefits of new high-throughput data-sources in the genetic dissection of complex traits.