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Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.

¹Department of Mathematics and Statistics, American University, Washington, DC, USA. dgerard@american.edu.

|August 10, 2021

Summary

Naive linkage disequilibrium (LD) calculations are biased by genotype uncertainty, especially in polyploids. New moment-based adjustments offer accurate, fast, genome-wide LD estimation, improving genomic analyses.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Linkage disequilibrium (LD) is crucial for genomic analyses like SNP pruning and decay estimation.
Genotype uncertainty causes significant bias in naive LD estimates, particularly in polyploid organisms.
Current accurate methods (maximum likelihood) are too slow for genome-wide applications.

Purpose of the Study:

To develop scalable and accurate methods for estimating linkage disequilibrium (LD) in the presence of genotype uncertainty.
To provide bias-corrected LD estimation suitable for genome-wide analyses, especially in polyploids.
To introduce a computationally efficient alternative to existing methods.

Main Methods:

Developed scalable moment-based adjustments to LD estimates using marginal posterior genotype distributions.
Evaluated methods on both simulated and real genetic datasets.
Implemented the methods in the R package 'ldsep'.

Main Results:

Moment-based LD estimators demonstrate accuracy comparable to maximum likelihood estimation.
These new estimators are significantly faster than maximum likelihood, approaching the speed of naive methods.
The approach effectively reduces attenuation bias caused by genotype uncertainty.

Conclusions:

Scalable, bias-corrected LD estimation is now feasible for genome-wide applications.
The moment-based method provides a computationally efficient and accurate solution for LD estimation with genotype uncertainty.
The 'ldsep' package offers a valuable tool for researchers in population genetics and genomics.