Mendelian Recurrence Risk Calculation

Mendelian recurrence risk calculation derives the probability that a single-gene disorder will recur from the laws of segregation, then refines that baseline using all available information through Bayesian analysis. A simple ratio — one half for an autosomal dominant carrier's child, one quarter for autosomal recessive parents — is only a starting point that pedigree structure, age, and test results can sharply modify.

Definition

Mendelian recurrence risk calculation is the estimation of recurrence probability for a single-gene disorder from segregation ratios, modified by Bayesian conditional probabilities that incorporate pedigree, phenotype, age, and test information.

Scope

This entry covers the segregation ratios for the principal Mendelian patterns and the Bayesian framework that combines a prior probability with conditional information to yield a posterior (final) risk. It is a methodological reference and does not provide risk figures for any individual consultand.

Core questions

• What baseline recurrence does each Mendelian inheritance pattern imply?
• How does Bayesian analysis combine prior risk with conditional evidence such as unaffected status or a normal test?
• How do prior, conditional, joint, and posterior probabilities relate in a risk table?

Key concepts

• Segregation ratios (1/2, 1/4)
• Autosomal dominant, recessive, and X-linked patterns
• Prior probability
• Conditional probability
• Joint and posterior probability
• Bayes' theorem in counseling
• Effect of unaffected offspring on carrier risk

Mechanisms

The calculation begins with a prior probability from Mendelian segregation — for example, the 1/2 chance that the child of an autosomal dominant carrier inherits the allele. Bayesian analysis then multiplies this prior by conditional probabilities that reflect observed evidence, such as several unaffected children (which lowers a woman's prior carrier risk for an X-linked disorder) or a normal molecular test. Dividing each pathway's joint probability by their sum gives the posterior, or final, risk. This structure lets independent pieces of evidence be combined coherently into a single number.

Clinical relevance

Bayesian recurrence calculation is a core competency in clinical genetics and explains why two consultands with the same family history can carry different final risks. This entry describes the method; it is reference material and not a substitute for individualized clinical assessment or genetic counseling.

Epidemiology

The approach applies to conditions following recognized Mendelian patterns — autosomal dominant, autosomal recessive, and X-linked recessive disorders — where baseline ratios are fixed but final risks vary with pedigree information, carrier frequencies, and the sensitivity of available genetic tests.

History

Bayesian reasoning entered genetic counseling in the mid-twentieth century, with Edmond Murphy and Gary Chase among those who systematized its use for carrier-risk problems. Worked-example texts by Bridge and Young then made the prior-conditional-posterior table a standard tool, and the advent of molecular testing added powerful new conditional terms to the calculation.

Key figures

• Thomas Bayes
• Edmond Murphy
• Ian Young
• Peter Bridge

Seminal works

• young-2007
• bridge-1997

Frequently asked questions

Why is the child's recurrence risk not simply the Mendelian ratio?

The Mendelian ratio is only the prior probability; Bayesian analysis adjusts it using additional information such as unaffected relatives or normal test results, which can substantially raise or lower the final risk.

What does a Bayesian risk table contain?

It lists the competing hypotheses (for example, carrier versus non-carrier), their prior probabilities, the conditional probabilities of the observed evidence under each, the resulting joint probabilities, and the normalized posterior probabilities.