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Related Experiment Videos

Stoppage: an issue for segregation analysis.

S L Slager1, T Foroud, F Haghighi

  • 1Department of Psychiatry, College of Physicians and Surgeons at Columbia University and the New York State Psychiatric Institute, New York, NY, USA. slager@mayo.edu

Genetic Epidemiology
|March 20, 2001
PubMed
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Segregation analysis for diseases like autism can be biased by "stoppage," where parents have fewer children after an affected birth. Ignoring this family size effect can seriously skew genetic estimates.

Area of Science:

  • Genetics
  • Biostatistics
  • Medical Genetics

Background:

  • Segregation analysis commonly assumes family size distribution is independent of the segregation ratio.
  • This assumption is violated in diseases with early onset (e.g., autism) where parental reproductive decisions are affected by affected offspring.

Purpose of the Study:

  • To investigate the impact of "stoppage" (parents stopping childbearing after an affected birth) on segregation analysis.
  • To derive a likelihood function accounting for stoppage probability and ascertainment probability.
  • To assess the bias in estimating the segregation ratio (p) under different ascertainment schemes.

Main Methods:

  • Derived a full likelihood for segregation analysis incorporating stoppage probability (d) and ascertainment probability (pi).

Related Experiment Videos

  • Analyzed the impact of stoppage on the estimation of p under random and non-random ascertainment.
  • Employed a simulation study to quantify the bias in p estimates when stoppage is ignored.

    • Main Results:

    • The derived likelihood allows estimation of p when family size distribution is known.
    • Stoppage does not bias p estimates under random ascertainment.
    • Non-random ascertainment schemes can lead to significant bias in p estimates if stoppage is not considered.

    Conclusions:

    • Stoppage is a realistic factor in segregation analysis for certain complex diseases.
    • Ignoring stoppage can lead to serious and potentially intractable bias in genetic parameter estimation.
    • Accurate segregation analysis requires accounting for family size adjustments due to affected offspring.