A major goal of current human genome-wide studies is to identify the genetic basis of complex disorders. However, the availability of an unbiased, reliable, cost efficient and comprehensive methodology to analyze the entire genome for complex disease association is still largely lacking or problematic. Therefore, we have developed a practical and efficient strategy for whole genome association studies of complex diseases by charting the human genome at 100 kb intervals using a collection of 27,039 microsatellites and the DNA pooling method in three successive genomic screens of independent case-control populations. The final step in our methodology consists of fine mapping of the candidate susceptible DNA regions by single nucleotide polymorphisms (SNPs) analysis. This approach was validated upon application to rheumatoid arthritis, a destructive joint disease affecting up to 1% of the population. A total of 47 candidate regions were identified. The top seven loci, withstanding the most stringent statistical tests, were dissected down to individual genes and/or SNPs on four chromosomes, including the previously known 6p21.3-encoded Major Histocompatibility Complex gene, HLA-DRB1. Hence, microsatellite-based genome-wide association analysis complemented by end stage SNP typing provides a new tool for genetic dissection of multifactorial pathologies including common diseases.

Microsatellites or short tandem repeats (STRs) are abundant in the human genome with easily assayed polymorphisms, providing powerful genetic tools for mapping both Mendelian and complex traits. Microsatellite genotyping requires detection of the products of polymerase chain reaction (PCR) amplification by electrophoresis, and analysis of the peak data for discrimination of the true allele. A high-throughput genotyping approach requires computer-based automation at both the detection and analysis phases. In order to achieve this, complicated peak patterns from individual alleles must be interpreted in order to assign alleles. Previous methods consider limited types of noise peaks and cannot provide enough accuracy. By pattern recognition of various types of noise peaks, such as stutter peaks and additional peaks, we have achieved an overall average accuracy of 94% for allele calling in our actual data. Our algorithm is crucial for a high-throughput genotyping system for microsatellite markers by reducing manual editing and human errors.

Our goal is to incorporate state-of-the-art partial evaluation in a library of general-purpose algorithms -in particular, mathematical algorithms -in order to allow the automatic creation of efficient, special-purpose programs. The main goal is efficiency: a specialized program often runs significantly faster than its generic version.This paper shows how a binding-time analysis can be used to identify potential sources for specialization in mathematical algorithms. The method is surprisingly simple and effective. To demonstrate the effectiveness of this approach we used an automatic partial evaluator for Fortran that we developed. Results for five well-known algorithms show that some remarkable speedup factors can be obtained on a uniprocessor architecture.

Microsatellites provide powerful genetic tools for complex disease mapping. Microsatellite genotyping requires analyzing peak data for discrimination of the true allele. In a previous study, we developed a new algorithm for automated genotyping. Here, we evaluate our algorithm's robustness. First, we found that our algorithm calculates the model parameter of noise peaks appropriately and infers genotypes correctly even with low selectivity and specificity in the intermediate result of its first step. Our results indicate the model robustly calculates noise peaks. Second, our algorithm adequately infers true allele peaks for small sample sets. Furthermore, we evaluated its potential risk of failing to construct noise peak model.