Recurrence Risks

Figure 16-1 Pedigree of a family with an autosomal recessive condition. The probability of being a carrier is shown beneath each individual symbol in the pedigree.

In contrast to single-gene disorders, the underlying mechanisms of inheritance for most chromosomal or genomic disorders and complex traits are unknown, and estimates of recurrence risk are based on previous experience (Fig. 16-2). This approach to risk assessment is valuable if there are reliable data on the frequency of recurrence of the disorder in families and if the phenotype is not heterogeneous. However, when a particular phenotype has an undetermined risk or can result from a variety of causes with different frequencies and with widely different risks, estimation of the recurrence risk is hazardous at best. In a later section, the estimation of recurrence risk in some typical clinical situations, both straightforward and more complicated, is considered.


Figure 16-2 Empirical risk estimates in genetic counseling. A family with no other positive family history has one child affected with a disorder known to be multifactorial or chromosomal. What is the recurrence risk? If the child is affected with spina bifida, the empirical risk to a subsequent child is approximately 4%. If the child has Down syndrome, the empirical risk for recurrence would be approximately 1% if the karyotype is trisomy 21, but it might be substantially higher if one of the parents is a carrier of a Robertsonian translocation involving chromosome 21 (see Chapter 6).

Risk Estimation by Use of Mendel’s Laws When Genotypes Are Fully Known


Figure 16-3 Series of pedigrees showing autosomal recessive inheritance with contrasting recurrence risks. A and B, The genotypes of the parents are known. C, The genotype of the consultand’s second partner is inferred from the carrier frequency in the population. D, The inferred genotype is modified by additional pedigree information. Arrows indicate the consultand. Numbers indicate recurrence risk in the consultand’s next pregnancy.

Risk Estimation by Use of Conditional Probability When Alternative Genotypes Are Possible

Of course, if the husband really were a carrier, the chance that the child of two carriers would be a homozygote or a compound heterozygote for mutant CF alleles is one in four. If the husband were not a carrier, then the chance of having an affected child is zero. Suppose, however, that one cannot test his carrier status directly. A carrier risk of 1 in 22 is the best estimate one can make for individuals of his ethnic background and no family history of CF without direct carrier testing; in fact, however, a person either is a carrier or is not. The problem is that we do not know. In this situation, the more opportunities the male in Figure 16-3C (who may or may not be a carrier of a mutant gene) has to pass on the mutant gene and fails to do so, the less likely it would be that he is indeed a carrier. Thus, if the couple were to come for counseling already with six children, none of whom is affected (Fig. 16-3D), it would seem reasonable, intuitively, that the husband’s chance of being a carrier should be less than the 1 in 22 risk that the childless male partner in Figure 16-3C was assigned on the basis of the population carrier frequency. In this situation, we apply conditional probability (also known as Bayesian analysis, based on Bayes’s theorem on probability published in 1763), a method that takes advantage of phenotypic information in a pedigree to assess the relative probability of two or more alternative genotypic possibilities and to condition the risk on the basis of that information. In Figure 16-3D, the chance that the second husband is a carrier is actually 1 in 119, and the chance that this couple would have a child with CF is therefore 1 in 476, not 1 in 88, as calculated in Fig. 16-3C. Some examples of the use of Bayesian analysis for risk assessment in pedigrees are examined in the following section.

Conditional Probability


Figure 16-4 Modified risk estimates in genetic counseling. The consultands in the two families are at risk for having a son with hemophilia A. In Family A, the consultand’s mother is an obligate heterozygote; in Family B, the consultand’s mother may or may not be a carrier. Application of Bayesian analysis reduces the risk for being a carrier to only approximately 3% for the consultand in Family B but not the consultand in Family A. See text for derivation of the modified risk.

In Family B, however, the consultand’s mother (individual II-2) may or may not be a carrier, depending on whether she has inherited a mutant F8 allele from her mother, I-1. If III-5 were the only child of her mother, III-5’s risk for being a carrier would be 1 in 4, calculated as image (her mother’s risk for being a carrier) × image (her risk for inheriting the mutant allele from her mother). Short of testing III-5 directly for the mutant allele, we cannot tell whether she is a carrier. In this case, however, the fact that III-5 has four unaffected brothers is relevant because every time II-2 had a son, the chance that the son would be unaffected is only 1 in 2 if II-2 were a carrier, whereas it is a near certainty (probability = 1) that the son would be unaffected if II-2 were, in fact, not a carrier at all. With each son, II-2 has, in effect, tested her carrier status by placing herself at a 50% risk for having an affected son. To have four unaffected sons might suggest that maybe her mother is not a carrier. Bayesian analysis allows one to take this kind of indirect information into account in calculating whether II-2 is a carrier, thus modifying the consultand’s risk for being a carrier. In fact, as we show in the next section, her carrier risk is far lower than 50%.

Identify the Possible Scenarios

A. II-2 is a carrier, but the consultand is not.

B. II-2 and the consultand are both carriers.

C. II-2 is not a carrier, which implies that the consultand could not be one either because there is no mutant allele to inherit.

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Nov 27, 2016 | Posted by in GENERAL & FAMILY MEDICINE | Comments Off on Recurrence Risks

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