© 2005 American Thoracic Society doi: 10.1164/rccm.2501002
From Quantitative Trait Locus to GeneA Work in ProgressNational Institute of Environmental Health Sciences and Department of Medicine Duke University Medical Center Durham, North Carolina Genetic background is an important determinant of many pulmonary diseases. Although single-gene (mendelian) disorders have been identified (1), most diseases are quantitative or complex, the result of an interaction between multiple genes. Because the contribution of each gene in a complex trait is relatively minor, identification of each of the genes that ultimately determine a complex trait is a major challenge. Furthermore, susceptibility genes interact with multiple environmental exposures or stimuli important in the etiology of a disease, and these interactions may vary from one population to another. Despite the enormous complexity of pulmonary diseases, results from studies, such as that presented by Reinhard and colleagues (pp. 880–888), have substantially enhanced our understanding of genetic contributions to physiologic and pathophysiologic changes in the lung (2). Traditionally, two broad research strategies have been used to identify loci or genes that determine disease susceptibility. The first incorporates meiotic mapping and positional cloning. Meiotic, or linkage, mapping exploits within-family associations between marker alleles and putative trait-influencing alleles that arise within families and may be followed by methods of cosegregation analyses (3). This approach is designed to identify association of a quantitative trait locus (QTL) within the entire genome that may contain one or more genes that are polymorphic and may account for the differential response phenotype under study. That is, meiotic mapping or positional cloning assumes no a priori hypothesis about the role of a specific gene or genes. The second main approach for gene discovery is to focus on a candidate gene or genes using a case-control design (association study). In this approach, genes are chosen a priori as likely biological candidates involved in the mechanisms that are believed to determine the phenotype of interest, and as such, become potential targets for genetic susceptibility. With the advent of high-density polymorphic maps that reveal gene sequence variation across the genome, interrogating the sequence variants between two different phenotypic strains for a number of gene candidates is realistic. Moreover, as the genotyping capabilities expand, it will be feasible to identify the genetic variation across thousands of genes in one single assay. Although the strength of linkage analysis is that it covers the entire genome, its weakness is that relatively large chromosomal intervals may associate with particular phenotypes. For instance, initial linkage analyses identify significant QTLs that may be as large as 10 to 20 cM and include hundreds or thousands of genes. Therefore, investigators must reduce the QTL to a more "manageable" size that will ultimately lead to identification of a gene that determines the phenotype of interest. In contrast, although the strength of the candidate gene approach is that it is focused on a specific gene that is pathogenically related to a phenotype, the results of this approach may be influenced by unique (genetic, behavioral, or environmental) characteristics of the study population/strain. Moreover, employing association studies alone could implicate certain genes in the expressed phenotype of interest, but other important loci that determine a phenotype may be missed. Given the multiple comparisons that are inherent to linkage and association studies, both approaches are at risk of type I error (false-positive). Clearly, neither the genetic linkage approach nor association study is independently sufficient to reach a clear understanding of the genetic basis of complex diseases. As one considers the contribution of Reinhard and colleagues (2), one must ask how to effectively move from the QTLs that they found to be associated with lung function to specific genes that control lung physiology. In fact, genomewide linkage scans have yielded few significant findings in complex human diseases (multigenic). The reasons for this general lack of success are numerous, but the primary reason is that, for most complex traits, no single locus confers a high degree of risk. Current technologies and statistical methods are not sufficient to permit fine mapping of the location of multiple genes that influence a complex disorder in genetically heterogeneous species like humans, particularly when environmental exposure may vary from one study population to the next. Although fine mapping of a QTL can decrease the size to about 1 million base pairs, it is unlikely that one can expect to narrow the interval further. A narrowly defined QTL will enable one to select and directly sequence candidate genes based on their position and/or biological relevance to the phenotype of interest. When sequence is available (e.g., between inbred strains of mice), one can examine regions of sequence variation to determine whether these regions of DNA might account for the phenotypic variation. Ultimately, proof that a gene variant causes a particular phenotype involves recapitulating the biological or pathophysiologic phenotype by manipulating the gene in vitro or in vivo. Silencing the gene in vitro via short inhibitory sequences of RNA, dominant negatives, or DNA mutagenesis have become standard approaches to determine the biological role of a particular gene. Adding back the allelic variant provides additional information. However, when considering complex diseases or phenotypes, such as lung function, molecular biology is not sufficient. Thus, focusing on the synteny (i.e., when two or more genes are located on the same chromosome with or without linkage) between humans and other model systems can accelerate the discovery of genes that truly affect physiology and pathophysiology. A unifying concept for these studies is the direct genetic relationship between humans and many model systems, including mice. That is, highly significant homologies in gene order and chromosomal structure have been maintained since the divergence of the human and mouse (4), and this similarity can be used to identify the pathophysiologic importance of genes in complex diseases. However, manipulation of genes is far easier in other model systems, such as Drosophila, zebrafish, Caernorhabditis elegans, and even yeast, and can provide important in vivo biological information, even though the physiologic relevance may prove to be limited. Although many QTLs have been reported, far fewer genes have been identified that substantially alter the risk of developing a disease. This is perhaps best illustrated for asthma, but applies to many other phenotypes. Thus, it is abundantly clear that we need more sophisticated approaches to move from QTL to gene. Focusing on these methodologic gaps will substantially accelerate the discovery of genes involved in complex human phenotypes, such as lung function. FOOTNOTES Conflict of Interest Statement: S.R.K. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript; D.A.S. is a consultant for Intermune, paid $16,000 each year, and this consultancy arrangement will end on April 1, 2005. REFERENCES
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