77.1 Major Genetic Approaches to the Study of Common Pediatric Disorders

John W. Belmont and Brendan Lee

A model for the genetic contribution to health is shown in Figure 77-1. Genetic variation that can have an impact on disease susceptibility is present in every person. Sometimes single gene mutations cause a condition such as cystic fibrosis or sickle cell anemia. But other genetic variations can contribute much less strongly to the emergence of specific medical conditions, and the effect can depend upon exposure to certain environmental factors. One goal in medical genetics is to identify genes that contribute to disease in the hope of preventing the occurrence of disease, either by avoiding inciting environmental factors or by instituting interventions that reduce risk. For persons who cross the threshold of disease, the goal is to better understand the pathogenesis in the hope that this will suggest better approaches to treatment. Common genetic variation can also influence response to medications and the risk of toxicities of various medications and environmental toxins.

Figure 77-1 Model for the influence of genetics on health. Everyone inherits some genetic liability for disease risk, but for multifactorial disorders this is insufficient to produce disease on its own. Over time, exposure to environmental factors leads from a presymptomatic to a disease state. Identifying the genes responsible for risk can lead to prevention strategies or treatments.

Complex traits may be inherently difficult to study if there are problems with the precision of clinical diagnosis. This is particularly true of neurobehavioral traits. A starting point in the genetic analysis of a complex trait is to obtain evidence in support of a genetic contribution and to estimate the relative strength of genetic and environmental factors. Complex traits typically exhibit familial clustering but are not transmitted in a regular pattern like autosomal dominant or recessive inheritance. Complex traits often show variation among different ethnic or racial groups, possibly reflecting the differences in gene variants among these groups.

Assessing the potential genetic contribution begins by determining whether the trait is seen among related individuals more often than in the general population. A common measure of familiality is the first-degree relative risk (usually designated by the symbol λ_s), which is equal to the ratio of the prevalence rate in siblings and/or parents to the prevalence rate in the general population. For example, the λ_s for type 1 diabetes is about 15. Collection of family data also allows the analysis of possible inheritance models using a method called segregation analysis. The relative strength of genetic and nongenetic risk factors can be estimated by variance components analysis, and the heritability of a trait is the estimate of the fraction of the total variance contributed by genetic factors (Fig. 77-2).

Figure 77-2 Heritability concept. The phenotypic variance of a particular trait can be partitioned between the contributions of the genetic variance, the environmental variance, and the measurement variance. This is usually empirically determined. Heritability is defined by the proportion of the phenotypic variance that is accounted for by the genetic variance. One can estimate the heritability from correlation of a quantitative trait between relatives.

It is not uncommon for a minority of cases to be caused by single gene mutations (mendelian inheritance), chromosomal disorders, and other genomic disorders. These less-common causes of the disease can often provide important insight into the most important molecular pathways involved. Chromosomal regions with genes that might contribute to disease susceptibility could theoretically be located with linkage mapping, which locates regions of DNA that are inherited in families with the specific disease. But practically, this has turned out to be quite difficult for most complex traits either because of a dearth of families or because the effect of individual genetic loci is weak.

Genetic association studies are more powerful in identifying common gene variants (>5% in the population) that confer increased risk of disease, but they fail if the disease-causing gene variants are relatively rare. Detection of the modest effect of each variant and interactions with environmental factors requires well-powered studies that often include thousands of subjects.

Linkage mapping and association studies require markers along the DNA that can be ascertained, or genotyped, with large-scale, high-throughput laboratory techniques. Markers that are typically used are in the form of microsatellites and single-nucleotide polymorphisms (SNPs; Fig. 77-3). Although humans all have the same genetic material, every person’s genome is slightly different. A sample of the same region of genome from about 50 people will reveal that about one in every 200 bases varies from the more common form. Although most SNPs lack any obvious function, a few alter the amino acid sequence of the protein or affect regulation of gene expression. Some of these functional alterations directly affect susceptibility to disease. A complex clinical phenotype can be defined by the presence or absence of a disease as a dichotomous trait, or by selection of a clinically meaningful variable such as body mass index in obesity, which is a continuous or quantitative trait.

Figure 77-3 Different combinations of single nucleotide polymorphisms (SNPs) are found in different individuals. The locations of these SNPs can be pinpointed on maps of human genes. Subsequently, they can be used to create profiles that are associated with difference in response to a drug, such as efficacy and nonefficacy.

(Adapted from Roses A: Pharmacogenetics and the practice of medicine, Nature 405:857–865, 2000. Copyright 2000. Reprinted by permission for Macmillan Publishers Ltd.)

Although it might not be possible to define subgroups of patients in advance based on common disease mechanisms, the more uniform the phenotype, the more likely that a genetic study will be successful. Locus heterogeneity refers to the situation in which a trait results from the independent action of more than one gene. Allelic heterogeneity indicates that more than one variant in a particular gene can contribute to disease risk. The development of a trait or disease from a nongenetic mechanism results in a phenocopy. These 3 factors often contribute to the difficulty in identifying individual disease-susceptibility genes because they reduce the effective size of the study population.

The probability that a person bearing any variant or allele (inherited unit, DNA segment, or chromosome) in a gene is affected with a specific disease has a certain probability. This is called the penetrance. Some diseases manifest signs only later in life (age-related penetrance), which could lead to misclassifying children who actually have the disease-producing gene as unaffected. Single-gene disorders are typically caused by mutations with relatively high penetrance, but some common variants have very low penetrance because their overall contribution to the disease is small. Many such common variants can contribute to disease risk for a complex trait.

Ideally, important environmental exposures should be measured and accounted for in a population because there may be a dependent interaction between the environmental factor and specific genetic variant. An example is the likely requirement for a viral infection preceding onset of type 1 diabetes. Although gene X environment interactions are strongly suspected to play an important role in common diseases, it is difficult to identify and measure them. Very large studies with uniform collection of information about environmental exposures are rare.

77.2 Current Understanding of Genetics of Common Disorders in Children