a population‐based study examining the contribution of sickle cell disease to the occurrence of developmental disabilities, the Miettinen formula (Equation 3.2) was applied to demonstrate that 34% of the cases of stroke‐related developmental disabilities in black children were due to sickle cell disease (Ashley‐Koch et al. 2001). In this example, the fraction of cases with stroke‐related disabilities with the risk factor (sickle cell disease) was 0.34 (13 children with sickle cell disease among a total of 38 children with stroke‐related developmental disabilities), and the relative risk was 130. Thus, the attributable fraction by the Miettinen formula is 34% (0.34 * (129/130)).
For all the ease with the family history approach, there are several pitfalls that may occur. One should be aware that a positive family history is a function of many things, including the frequency of the condition in the general population, the size of the family, the natural history of the condition, the underlying genetic mechanism, and possibly shared environmental exposures. For example, if a condition is relatively common in the population, a positive family history may simply result from the presence of phenocopies in the family. Also, in a condition with late onset, young relatives may be misclassified as “disease‐free” (i.e. they may not express the condition simply because they are too young to express it). For example, if one is studying Alzheimer disease and finds that a 55‐year‐old sibling of an affected proband has no signs of dementia, one should re‐examine that sibling at regular intervals to determine if he/she remains asymptomatic over time. It is possible that, at the age of 55 years, the sibling is too young to express symptoms. However, if he/she were re‐examined at 60 years of age, he/she would exhibit symptoms. Thus, if the only physical examination for that sibling was performed at 55 years of age, he/she would be classified as “normal.” But if another physical examination was performed at the age of 60 years, then the individual would be classified as “affected.” Thus, whenever resources allow, follow study participants over time for changes in affection status. In addition to variation in the age of onset of a condition, there may be variable expression of the condition. Therefore, family members who have minor manifestations of the condition or are in the early stages of the disease process may not be recognized as expressing the condition. Furthermore, in complex genetic disorders, individuals in the family may not express the disorder because they possess only a fraction of the necessary genetic and environmental factors to express the condition. For all these reasons, one may want to examine the association of family history with several types of relatives in order to make sure that the same conclusion is reached for all relative types.
Correlation Coefficients
Another simple approach to determining familial aggregation is the use of scatter plots. In the case of a quantitative trait, plotting the trait measurement of one relative against the trait measurement of another relative will provide the correlation of the trait among the pair. The slope of the line that is formed by the data is the square of the correlation coefficient. As shown later in the chapter, the correlation coefficient can also provide information regarding the heritability of a trait. Figure 3.3 examines the correlation of age of onset of Alzheimer disease among affected siblings. These data were randomly selected from a larger study examining the genetic contribution to the age at onset in Alzheimer and Parkinson disease (Li et al. 2002). The proband’s age of onset was plotted against the sibling’s age of onset. The resulting slope of the line was 0.16, corresponding to a Pearson correlation coefficient of 0.40 (p < 0.0001, n = 400 sibling pairs), suggesting that there is a significant correlation of age of onset among siblings affected with Alzheimer disease. Khoury and colleagues (1985) demonstrate the use of the loglinear model to determine correlation for qualitative traits.
Figure 3.3 Correlation of age of onset among siblings affected with Alzheimer disease.
Twin and Adoption Studies
Twin and adoption studies can be quite useful as they provide an opportunity to tease apart the role of genetics and a common familial environment. The most difficult aspect of these types of studies is obtaining reasonable sample sizes. This is especially true in the United States where twin, adoption, and disease registries are less common than in European countries.
The premise of twin studies is the comparison of the disease concordance in monozygotic (MZ) with dizygotic (DZ) twins. Since MZ twins share 100% of their genetic make‐up and DZ twins share on average 50% of their genetic make‐up, a greater disease concordance in MZ compared with DZ twins is consistent with the involvement of genetics. An advantage of this approach is that it controls for a common familial environment, but this is generally only applicable for exposures during childhood. It may not entirely control for prenatal exposures because the twins, especially DZ, may not have shared placentas, chorions, and amniotic sacs. However, the intrauterine and extrauterine environments are generally more similar for DZ twins than siblings. It is even less likely that adult exposures are similar among the twins, especially if they reside in different geographic locations. Other possible confounding variables controlled for in the twin study approach include age and sex (provided same‐sex DZ twins are utilized). However, keep in mind that for a condition with variable age of onset, it may be necessary to follow the twins for several years in order to conclusively determine that a set of twins is concordant or discordant for the condition. But most importantly, prior to beginning any twin study, one must determine the zygosity of the twins, as misclassification can have a devastating effect on the outcome of the analysis (Ellsworth et al. 1999). Often families will know the zygosity of the twins, but it is prudent to determine this experimentally via genotyping.
Table 3.2 below shows examples of concordance rates that might be observed under various disease etiologies. In practice, the results will not be easily interpreted for complex disorders, much like the results obtained in the last row of this table. In general, however, if the frequency in concordance is greater in MZ compared with DZ twins, it is accepted as evidence for at least a minor role of genetics in the disease etiology.
Adoption studies can also be used to examine the evidence for genetic versus common familial environmental factors. In this approach, cases are ascertained and then the frequency of the condition in the biological parents is compared with the frequency of the condition in the adoptive parents. As shown below in Table 3.3, a higher disease frequency in biological parents argues for the presence of genetic factors, while a higher disease frequency in the adoptive parents argues for the presence of a common familial environmental factor. A variation on adoption studies is the adoptive‐twin approach where twins who have been reared apart are examined to determine if genetics (high concordance rate) or environment (low concordance rate) plays a role in the disease etiology.
Table 3.2 The association between disease concordance rates in twins and disease etiology.
| Frequency of disease concordance in twins | ||
|---|---|---|
| MZ (%) | DZ (%) | Possible etiology |
| 85 | 85 | Common familial environment |
| 100 | 25 | Mendelian recessive genetic factor |
| 100 |
50
|
