Simple test of the Multifactorial-Polygenic Model with sex dependent thresholds

Area of Science:

• Quantitative genetics
• Human genetics
• Disease inheritance patterns

Background:

• Sex differences in population incidence are common for many diseases.
• The Multifactorial-Polygenic Model (MPM) explains diseases influenced by multiple genes and environmental factors.
• Understanding relative risk (RR) in families is crucial for genetic counseling.

Purpose of the Study:

• To examine the relationship between the sex ratio of population incidence and expected relative risk (RR) to first-degree relatives.
• To test the Multifactorial-Polygenic Model using sex-specific incidence data.
• To determine how incidence, liability correlation (r), and sex ratio affect RR.

Main Methods:

• Analysis of the Multifactorial-Polygenic Model under varying sex ratios.
• Calculation of expected relative risk (RR) for first-degree relatives.
• Examination of the influence of population incidence and liability correlation (r) on RR.

Main Results:

• Expected RR increases with increasing sex ratio and liability correlation (r).
• Relatives of the low-risk sex consistently show higher RRs than relatives of the high-risk sex.
• Population incidence variation has minimal impact on RR at a given sex ratio and r.
• Expected RRs are generally small, rarely exceeding two-fold.

Conclusions:

• The quantitative relationship between sex ratio and RR supports the Multifactorial-Polygenic Model.
• This analysis provides a method to test the MPM when sex-specific or severity-specific thresholds are identifiable.
• Findings highlight the importance of considering sex-specific risks in genetic studies.