Another Approach toward Complex Traits: Methods and Applications

Abstract

The mapping of complex traits is one of the central and most challenging areas of human genetics today. The evolution of technologies on DNA polymorphisms has led to the identifications of a large number of new markers, covering almost every tiny region of the human genome. For this reason the large-scale genome scans have become popular and common tools in the search for susceptibility genes for complex human disorders in recent genomic studies. 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. 　　A number of novel methods have been proposed during the past 20 years to detect association/linkage between markers and disease susceptibility loci. The success however has been largely restricted to simple Mendelian diseases. For common /complex human disorders, the progress has been slow, and results are often inconsistent and discouraging. This is due in part to the need for capable statistical methods that accommodate large amounts of correlated genotypic and phenotypic data. Most current methods that make use of marginal information only, fail to include the information of the interaction among the disease loci. It is thus less likely for these methods to have adequate power to find the muted genes. Since interactive information among markers reflects the joint information of the traits due to multiple genes (and perhaps other risk factors), we believe, mapping methodologies that are able to simultaneously inspect disjoint marker loci (possibly on different chromosomes) are crucial for the success of future genes mappings. In these talks, I shall present an entirely different approach to address these difficulties. I will first review the methods using family-trio data and several disease models, the backward haplotype transmission association (BHTA) algorithm, proposed in Lo and Zheng (2002). A realistic demonstration and findings using this approach from recent projects (Lo & Zheng, 2004) on IBD data will then be presented. The outcomes of the applications supported the idea that this alternative approach has the potential to draw substantial crucial information that leads to very interesting results. The methods that are applicable to other type of data and designs will be discussed. If time permits, the issues of multiple comparisons and statistical significance for a large number of tests will be discussed.