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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Interval mapping of quantitative trait loci with selective DNA pooling data.

Jing Wang¹, Kenneth J Koehler, Jack C M Dekkers

¹Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames, Iowa 50011, USA.

Genetics, Selection, Evolution : GSE

|December 7, 2007

Summary

New interval mapping methods (LS-pool and ML-pool) improve quantitative trait loci (QTL) detection using selective DNA pooling. These methods offer greater power and more accurate QTL position and effect estimates than single marker analysis.

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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

Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

Selective DNA pooling efficiently identifies quantitative trait loci (QTL) by analyzing marker allele frequencies in extreme phenotype groups.
Current single marker analysis methods detect QTL linkage but lack separate estimates for QTL position and effect, and do not leverage multi-marker data.

Purpose of the Study:

Develop and evaluate two novel interval mapping methods for selective DNA pooling data: least squares regression (LS-pool) and approximate maximum likelihood (ML-pool).
Compare the performance of LS-pool and ML-pool against single marker analysis and selective genotyping through simulation.

Main Methods:

Developed LS-pool and ML-pool methods for analyzing selective DNA pooling data.
Utilized multi-marker and multi-family information across various family structures (half-sib, F2, backcross).
Conducted simulations to compare method performance against single marker analysis and selective genotyping.

Main Results:

Both LS-pool and ML-pool demonstrated higher power for QTL detection compared to single marker analysis.
These methods provide independent estimates of QTL location and effect.
LS-pool and ML-pool showed comparable performance to selective genotyping in large families.
ML-pool reduced the bias in QTL location estimation observed with LS-pool in small families, particularly for distal QTL.

Conclusions:

LS-pool and ML-pool are powerful and accurate methods for QTL analysis in selective DNA pooling.
ML-pool offers improved QTL location estimation, especially in smaller family sizes.
These interval mapping approaches enhance the utility of selective DNA pooling for genetic studies.