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Surgical Correction for Pediatric Epiblepharon and Trichiasis

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Correcting for Ascertainment.

Warren Ewens¹, Robert C Elston²

¹Department of Biology, University of Pennsylvania, 3601 Locust Walk, Philadelphia, PA, 19104, USA. wewens@sas.upenn.edu.

Methods in Molecular Biology (Clifton, N.J.)

|October 6, 2017

Summary

This summary is machine-generated.

Human genetics studies often use non-random sampling, complicating statistical inferences. This work addresses ascertainment bias in genetic data analysis, particularly for large pedigrees, by exploring theoretical corrections for likelihood calculations.

Keywords:

Catchment area Complete ascertainment Multiple ascertainment Proband Proband sampling frame Proband-dependent sampling Pseudo-likelihood Sequential sampling Single ascertainment

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Area of Science:

Human Genetics
Statistical Genetics
Bioinformatics

Background:

Genetic studies frequently rely on non-random sampling, violating assumptions of standard statistical methods.
Likelihood-based inferences in human genetics are particularly sensitive to sampling biases, known as ascertainment.
Existing methods for correcting ascertainment bias are well-developed for sibship data but less so for larger pedigrees.

Purpose of the Study:

To address the challenges of ascertainment bias in human genetic data analysis.
To explore theoretical frameworks for correcting likelihoods in the presence of non-random sampling.
To discuss the complexities of ascertainment correction for large pedigrees.

Main Methods:

Review of established theories for correcting likelihoods based on sibship data.
Exploration of theoretical challenges in analyzing larger pedigrees with ascertainment.
Brief description of a general method used by genetic analysis software for ascertainment correction.

Main Results:

A theoretical foundation exists for correcting ascertainment bias in sibship data.
Ascertainment correction for large pedigrees is significantly more complex, especially with multiple ascertainment sources.
A general approach for software-based ascertainment correction in pedigree data is outlined.

Conclusions:

Accurate statistical inference in human genetics requires addressing ascertainment bias.
Further theoretical development is needed for robust ascertainment correction in complex pedigree structures.
While software exists, a comprehensive theoretical understanding of general ascertainment remains an active area of research.