ArticleHeredity2021
Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.
Article in Heredity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Tests for segregation distortion in higher ploidy F1 populations.G3 (Bethesda, Md.) · 2025Article
- Pre-breeding in alfalfa germplasm develops highly differentiated populations, as revealed by genome-wide microhaplotype markers.Scientific reports · 2025Article
- M-CSF Protects Against Ulcerative Colitis via Aconitate: Mendelian Randomization and Experimental Evidence.Journal of inflammation research · 2025Article
- Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa.Frontiers in plant science · 2024Article
- Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Comment on three papers about Hardy-Weinberg equilibrium tests in autopolyploids.Frontiers in genetics · 2022Article
- Variant calling in polyploids for population and quantitative genetics.Applications in plant sciencesArticle
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Abstract
Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https://cran.r-project.org/package=ldsep ).
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