The advantage of segregation analysis is that it can provide an inheritance model and parameters that may be used in parametric linkage analysis. However, generally segregation analysis can only model 1–2 loci, which may not be very useful for most complex diseases. That is, if none of the inheritance models examined in the segregation analysis can adequately accommodate the complexities of the underlying inheritance model of the disorder, even the “best‐fitting” model will not provide much information.
Additionally, this approach is extremely sensitive to ascertainment bias. In genetic analysis, families are often collected based on the presence of many affected individuals. Thus, for segregation analysis, there may be a high proportion of families with numerous affected individuals that are used in the analysis, when in reality these types of families only make up a small percentage of the disease population and most cases may be observed in families with only one or two affected individuals. For example, the probability that an affected individual will be ascertained as a proband is π, and when π = 1, all the individuals in the study population who have the condition have been ascertained. This is referred to as “complete ascertainment.” When the probability that an affected individual is a proband is very low (π approaches 0), each sibship is expected to have only a single proband. This is called “single ascertainment.” With this latter ascertainment approach, the probability that a family will come to the investigator’s attention, and be included in the study, is related to the number of affected individuals in the family. That is, the more affected the individuals in the family, the higher the likelihood that this particular family will be ascertained, thus introducing a biased distribution of family types in the analysis.
If one does not correct for the method in which the families were ascertained, the estimate of the segregation probability of the disease allele can be biased, which can affect the “best‐fitting” genetic model. In some cases, the ascertainment bias may be so great that it causes the investigator to incorrectly conclude that the disorder is consistent with a single‐gene model (Greenberg 1986). For a discussion on approaches for correcting ascertainment bias, see Khoury et al. (1993).
There are several analytic approaches to segregation analysis, each with its strengths and weaknesses. The most commonly used methods include the mixed model (Morton and MacLean 1974; Lalouel and Morton 1981; MacLean et al. 1984), the transmission probability model (Elston and Stewart 1971), the unified model (Lalouel et al. 1983) which draws on the strengths of both the mixed and transmission probability models, and the regressive model (Bonney 1984). All of these approaches are computationally intensive but are available for use in several software packages such as POINTER (Lalouel and Morton 1981), SAGE (http://darwin.cwru.edu/sage/) and PAP (Hasstedt 1993).
Segregation analysis is not widely used in the evaluation of complex diseases because it is susceptible to the presence of genetic heterogeneity, phenocopies, gene–gene and gene–environment interactions, which are quite difficult to model. Consequently, segregation analysis results that support the involvement of a major gene in a condition are much easier to interpret than results that do not support such an effect. This is especially true if there are other data to support the involvement of genetics, such as twin data or familial clustering. In spite of these difficulties, there are examples of the successful application of segregation analysis to complex disorders, even to psychiatric disorders that tend to have the additional complexity of defining a precise phenotype for investigation (evidence for a Mendelian genetic factor contributing to obsessive compulsive disease and (Nestadt et al. 2000; Notarnicola et al. 2000).
Summary
The analyses presented in this chapter will aid in determining whether genes play an important role in a condition or trait. It is important to establish a genetic basis before embarking on more elaborate analyses such as whole genome scans using linkage and/or association analysis because of the ethical, financial, and labor challenges associated with them. However, the first step in the analytic process should be a thorough examination of the literature. Often, because complex disorders are common and of public health importance, many of the analyses discussed in this chapter will have been previously completed by other investigators. Therefore, there may already exist sufficient evidence to support the involvement of genetics in the etiology of the disorder under investigation but evaluate the literature critically, keeping in mind how the study population (including age and ethnicity) and phenotype was defined in each of the published analyses. Significant deviations from the definition of one’s own study population and phenotype may suggest that prior results do not generalize to the present study and may necessitate repeating of the analyses described in this chapter.
If one determines that the types of analyses presented in this chapter (or any of the other chapters) must be performed, give careful consideration to study design prior to collecting and analyzing the data. Study design will influence the types of biases introduced into the data and can limit the types of analyses that may be performed.
Pay careful attention to the types of bias in the data. Ignoring ascertainment biases and confounding factors (age, ethnicity, etc.) may lead one to the wrong conclusions. If control samples are utilized, one must ensure that they are properly matched to the cases for confounding factors. When historical data are obtained on family members, follow up with examination of medical records or physical examination of those individuals to corroborate the information whenever resources allow. If a late‐onset disorder is being studied, re‐examine “unaffected” individuals after a period of time to confirm that they are truly unaffected and not misclassified because they were too young to express the disorder.
Keep in mind that for complex genetic disorders, there may be a high percentage of the patient population for whom the disorder is sporadic (no other affected family members), and the genetic mechanisms that lead to complex disorders may or may not be the same in familial and sporadic cases. For example, the BRCA1 gene that has been implicated in familial cases of breast cancer is not frequently mutated in cases of sporadic breast cancer (Easton et al. 1993; Futreal et al. 1994). This caveat is also important when comparing genetic mechanisms for early‐onset versus late‐onset familial versions of a disorder.
Finally, recognize that statistical associations for familial clustering may not represent a genetic etiology for a disorder but may be due to a common familial environment, or simply chance. The definitive evidence that a disorder is genetic is the identification of the specific genetic variations that lead to the condition.
References
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