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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Maximum likelihood parentage assignment using quantitative genotypes.

Matthew Gray Hamilton^1,2

¹CSIRO Aquaculture, CSIRO Agriculture and Food, Castray Esplanade, Hobart, TAS, Australia. M.Hamilton@cgiar.org.

|March 11, 2021

Summary

This study introduces a new quantitative maximum likelihood method for parentage assignment in pooled DNA samples. This approach significantly improves accuracy and reduces costs for genetic studies and breeding programs.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Parentage assignment is crucial for selective breeding and molecular ecology.
Current methods are costly and often limited to individual samples.
Pooling samples can reduce costs but requires specialized analytical approaches.

Purpose of the Study:

To develop and validate a novel maximum likelihood (ML) parentage assignment method for pooled samples.
To enable accurate parentage assignment using low-density single nucleotide polymorphism (SNP) quantitative genotype data.
To reduce the cost and expand the applicability of parentage assignment.

Main Methods:

Developed a 'quantitative maximum likelihood' (QML) method for pooled samples.
Utilized low-density single nucleotide polymorphism (SNP) quantitative genotype data.
Validated the method with simulated data and compared it to existing approaches.

Main Results:

The QML method accurately assigns parentage to pooled samples.
QML demonstrates higher accuracy than discrete genotype ML methods, exclusion methods, and weighted least squares.
The method is applicable to both 'pooling-for-individual-parentage-assignment' and 'pooling-by-phenotype' strategies.

Conclusions:

Quantitative maximum likelihood offers a cost-effective and accurate solution for parentage assignment in pooled samples.
This method enhances the feasibility of large-scale genetic studies and breeding programs.
QML has the potential to significantly reduce costs, even for small pool sizes.