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Any parent who has taken young children on a car trip will understand the question in the title and its implied impatience with the duration of the journey. The same question can be put to the research community’s journey toward understanding the genetic basis of complex disease. Recently developed genomic technologies, such as oligonucleotide microarrays and achievements including whole genome sequencing, have suggested that scientists can now analyze complex genetic diseases at a much more rapid pace. The analytic speed is further increased by the large amount of genetic and genomic information that is available in public databases, which enables several analytic steps to be computationally performed. However, it is clear that we have not yet arrived at our desired destination, that of knowing the genetic basis for complex disease susceptibility (). Therefore, it is an appropriate time to ask if complex disease research is moving in the right direction and if it is using the best road map and the appropriate type of transportation.

Variability may be introduced in an observed phenotype by a range of elements. Inherited genetic factors, as well as environmental and behavioral conditions, may affect the phenotype. The blend of all these interactions gives rise to the unique being every living creature is. Experimental genetics has traditionally been, and still is, a very powerful tool for dissecting the genetic factors out of the blend that results in the observed phenotype complexity.

A number of significant discoveries have resulted from genetic analysis of model experimental organisms. Improved methods for quantitative trait analysis, a process referred to as quantitative trait locus (QTL) mapping, have enabled investigators to make genetic discoveries. This mapping method requires the experimental generation of intercross progeny derived from two selected parental strains, chosen because they differ in a trait of interest. Through correlative analysis of the measured phenotype and genotype at multiple positions in the genome for each intercross progeny, regions of the genome responsible for the differences in the trait are identified. The genomic regions that quantitatively contribute to the trait are referred to as QTL. QTL analysis has been successfully used to map important traits in crop plants, cattle, fruit flies, mice, and many other model organisms. The statistical basis for QTL mapping has been thoroughly investigated (reviewed in ref. ). Based on this statistical underpinning, experimental crosses using model organisms can be designed to reliably detect QTLs, even when the involved regions make a relatively small contribution to the trait being studied.

Commonly available inbred mouse strains can be used to genetically model traits that vary in the human population, including those associated with disease susceptibility. In order to understand how genetic differences regulate trait variation in humans, we must first develop a detailed understanding of how genetic variation in the mouse produces the phenotypic differences among inbred mouse strains. The information obtained from analysis of experimental murine genetic models can direct biological experimentation, clinical research, and human genetic analysis. This “mouse to man” approach will increase our knowledge of the genes and pathways regulating important biological processes and disease susceptibility.

The identification of genes affecting complex traits (i.e., biological traits affected by several genetic and environmental factors) is a very difficult and challenging task (–). For many complex traits, the observable variation between individuals is quantitative; hence, loci affecting such traits are generally termed quantitative trait loci (QTLs). In contrast with monogenic traits, it is impossible to identify all the genomic regions responsible for complex trait variation without additional information on how these regions segregate (,). A key development in complex trait analysis was the establishment of large collections of molecular/genetic markers. With the discovery of a large amount of single nucleotide polymorphisms (SNPs) in human and model organisms, correlating SNP markers with phenotype in a segregating population has become a useful tool in QTL studies (). In both linkage and association mapping, the development of high-throughput methods to discover and genotype polymorphism markers has enabled whole-genome scanning to detect individual loci possible ().

Common lung diseases are likely to be multifactorial and multigenic. In addition, the lung exhibits a limited set of biological and physiological responses, so different lung diseases exhibit significant overlap in phenotype. This complexity in the development and manifestation of lung disease poses significant challenges for developing complete and accurate models of disease. Nonetheless, a layered strategy that includes in vitro and in vivo systems can offset these limitations. In vitro systems have evolved from simple organ culture to intricate procedures for cell culture that exhibit high fidelity to behavior in vivo. Similarly, in vivo systems have evolved from traditional physiology-based models in large animals and rodents to genetic modification of mice using targeted and conditional systems. Complex traits may be studied in inbred, recombinant, or congenic strains of mice, and single gene effects may be segregated naturally or experimentally. Ultimately, results from these in vitro and in vivo models identify candidate genes for further study in humans.

Osteoporosis is a disease characterized by an inadequate amount and/or faulty structure of bone, which increases the susceptibility to fracture with minimal trauma. Osteoporotic fractures are most commonly observed among the elderly. Yet, the pathogenesis of osteoporosis starts early in life, leading some researchers to view osteoporosis as a pediatric disease (). Considerable past research has centered on the influence of reproductive, nutritional, and/or life-style factors on the development of osteoporosis. With the advent of new molecular genetic approaches, the focus of research has recently shifted toward genetic factors. Genetic epidemiological studies provide convincing descriptive data including population and ethnic differences, studies of familial aggregation, familial transmission patterns, and comparisons of twin concordance rates that tell a significant part of how the vulnerability to developing osteoporosis is inherited (,). Almost certainly, the development of osteoporosis will be found to involve a complex interplay between both genetic and environmental factors that are difficult to control in complex populations.

Most behavioral traits operate on a phenotypic and genetic continuum, i.e., the phenotypic output is quantitative based on the genetic input. No one gene is either necessary or sufficient to account for the observed phenotype; rather, a collection of genes is responsible. This phenotypic and genetic complexity is particularly evident in psychological disorders. For instance, first-degree relatives of schizophrenics have a 9% risk for a diagnosis, whereas the risk drops to 2% for a third-degree relative (. These findings suggest that many genes contribute, and as the proportion of shared genes increases among relatives, so does the likelihood of shared diagnosis. Regardless of commonalities among genotypes, phenotypic expression may vary significantly in the frequency and severity of symptoms. This further supports the contention that several genes contribute to the trait, each with small effects.

Most behavioral responses to alcohol are known to be influenced by genetic factors. Human twin and adoption studies consistently show that susceptibility to alcohol abuse is heritable (). The mode of inheritance is unknown, but is certainly polygenic and multifactorial, with a substantial environmental effect (,). Despite much research, the genes and causal pathways determining susceptibility to alcohol abuse and dependence remain relatively unknown. Identifying genes that mediate alcoholism will improve strategies for diagnosis, treatment, and ultimately prevention.

The human leukocyte antigen (HLA) region, on chromosome 6p21.3, contains more than 200 genes within this 3 Mb segment, many of which are involved in the function of the immune system (). The HLA class I (HLA-A, B, and C) and class II (DRB1, DQB1, DPB1, and DQA1) loci encode cell surface heterodimeric proteins that bind antigenic peptides and are the most polymorphic genes in the human genome ( Fig. 1 for HLA region map). Moreover, most of this extensive allelic sequence diversity (i.e., >500 alleles at the HLA-B locus and >300 at the DRB1 locus) is functional and affects peptide binding and recognition of the HLA-peptide complex by the T-cell receptor. Statistical analysis of HLA class I and class II sequences has indicated, based on the ratio of nonsynonymous to synonymous substitutions in the polymorphic sequences encoding the peptide binding cleft of all class I and class II loci, that these polymorphic sequences have been subjected to balancing selection (, ). Analyses of allele frequency distributions () in various human populations have also supported the action of balancing selection for all HLA loci, with the exception of DPB1 (). However, the Ewens-Watterson test (), examining allele frequency distributions, is a relatively insensitive test for balancing selection, and the ratio of nonsynonymous to synonymous substitutions for DPB1 is consistent with balancing selection (). Therefore, the DPB1 polymorphism is probably not neutral, but the selective pressures operating on DPB1 do appear to be different from those shaping the allele frequency distributions of the other HLA loci.