Genetic Analysis of Complex Disease. Группа авторовЧитать онлайн книгу.

Figure 3.2 Example of ascertainment bias in genetic analysis when ascertaining through an affected individual.

Clearly, ignoring the ascertainment scheme used in an analysis can lead to false conclusions. For example, in the fragile X literature, it had been reported that male offspring of premutation and full‐mutation mothers received larger CGG repeat expansions than did female offspring (Rousseau et al. 1994; Loesch et al. 1995). However, once potential sources of ascertainment bias were removed, it was determined that there was no association between sex of the offspring and the size of the mutation inherited from the mother (Ashley‐Koch et al. 1998). In this example, there were three types of ascertainment bias that were sequentially removed from the analysis. First, in clinically ascertained families, the transmission to the proband was removed. This is a standard ascertainment correction known as the Weinberg correction scheme (Weinberg 1912). Second, transmissions were excluded where not all individuals within a sibship were tested. That is, in fragile X families, nonsymptomatic females are tested more often than nonsymptomatic males because only the females are at risk to have affected offspring. This ascertainment bias increases the number of transmissions to premutation females compared with premutation males. Finally, the transmissions of the CGG repeat to the mothers of the proband were omitted. Because full‐mutation females have reduced fitness (Sherman et al. 1984), mothers of probands are more likely to be premutation carriers merely because they have reproduced. Consequently, including transmissions of the CGG repeat from carrier grandmothers to mothers of probands will also increase the number of transmissions to premutation females. Table 3.1 shows how sequentially correcting for the ascertainment bias in fragile X families changes the conclusion regarding the association between offspring sex and the size of the CGG expansion. Notice that the statistical tests (t‐test and logistic regression) become insignificant as the correction schemes are applied. Additionally, it illustrates that in some cases, standard correction schemes, such as the Weinberg method, are not sufficient to abolish the bias in the ascertainment scheme. Thus, all potential sources of bias in the data set must be carefully considered.

Table 3.1 Association between sex of offspring and risk of expansion in fragile X syndrome.

      Source: From Ashley‐Koch et al. (1998); reprinted with permission.

Approaches to Determining the Genetic Component of a Disease

      King et al (1984) described the steps necessary to define the genetic mechanisms involved in a disease or trait many years ago, but those steps are still valid today. First, the evidence for a familial component to the condition must be established. Next, the cause of familial aggregation must be determined. That is, clustering in a disease may result from common environmental factors, rather than genetic factors, and these two hypotheses must be evaluated. Finally, the specific genetic factors must be identified and the manner in which they interact with each other and with environmental factors to contribute to the disease etiology must be defined.

      Below, several approaches are presented to evaluate whether or not genes contribute to the etiology of a disease, and to quantitate the contribution of those genes to the disease etiology.

      Co‐segregation with Chromosomal Abnormalities and Other Genetic Disorders

      While complex disorders generally do not exhibit a recognizable inheritance pattern, occasionally in a subset of patients, a complex disorder will segregate with a cytogenetic abnormality or another known genetic disorder. These associations provide valuable information regarding the location of at least one locus involved in the disease etiology. For example, individuals with trisomy 21, or Down syndrome, have an increased risk for developing Alzheimer disease. This increased risk is due to amyloid plaques resulting from an increased dosage of the amyloid precursor protein (APP) (Rumble et al. 1989). Thus, it was not surprising to find that a subset of families with early‐onset Alzheimer disease are linked to chromosome 21 and segregate mutations in the APP gene (St George‐Hyslop et al. 1987; Goate et al. 1991). Another example of co‐segregation of a complex disease and cytogenetic abnormality is the association between autistic disorder and 15q11‐q13 abnormalities. There are numerous examples of isolated patients with autistic disorder and duplications or inversions involving 15q11‐q13 (Wolpert et al. 2000). In many cases, the de novo rearrangements are thought to be maternal in origin (Lindgren et al. 1996). In addition, in the absence of these cytogenetic abnormalities, families with two or more individuals exhibiting autistic disorder display evidence for linkage (Philippe et al. 1999; Bass et al. 2000) and linkage disequilibrium (Cook, Jr. et al. 1998; Martin et al. 2000a; Menold et al. 2000) in this region. Although as yet no gene has been identified, the convergence of cytogenetic, linkage, and association data suggest that a locus involved in susceptibility to autistic disorder is located at 15q11‐q13.

      Familial Aggregation

      One of the characteristics of a genetic disorder is that it aggregates, or clusters, within families. If familial clustering of a disorder is observed, there are several approaches described below to determine if this observation is statistically significant. However, keep in mind that familial clustering may also be due to a common familial environment or simply due to chance. So, while statistical evidence of familial clustering may support the involvement of genetics in the disorder under investigation, it will be necessary to identify the underlying genes to confirm this.

      Family History Approach

In this approach, one ascertains the presence or absence of family history in study participants (with and without the condition) and then tests for statistically significant associations between family history and the condition. Khoury et al. (1993) defines three variations on this approach, depending on the level of detail obtained on the relatives. The Abbreviated Family History Approach involves simply questioning the study participants on the presence or absence of the condition in other family members. This method is by far the fastest and cheapest, but also the most susceptible to misclassification