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. 2023 Aug 30;13(9):jkad154.
doi: 10.1093/g3journal/jkad154.

GWLD: an R package for genome-wide linkage disequilibrium analysis

Rong Zhang  1 , Huaxuan Wu  1 , Yasai Li  1 , Zehang Huang  1 , Zongjun Yin  2 , Cai-Xia Yang  1 , Zhi-Qiang Du  1
Affiliations

GWLD: an R package for genome-wide linkage disequilibrium analysis

Rong Zhang et al. G3 (Bethesda). .

Abstract

Linkage disequilibrium (LD) analysis is fundamental to the investigation of the genetic architecture of complex traits (e.g. human disease, animal and plant breeding) and population structure and evolution dynamics. However, until now, studies primarily focus on LD status between genetic variants located on the same chromosome. Moreover, genome (re)sequencing produces unprecedented numbers of genetic variants, and fast LD computation becomes a challenge. Here, we have developed GWLD, a parallelized and generalized tool designed for the rapid genome-wide calculation of LD values, including conventional D/D', r2, and (reduced) mutual information (MI and RMI) measures. LD between genetic variants within and across chromosomes can be rapidly computed and visualized in either an R package or a standalone C++ software package. To evaluate the accuracy and speed of LD calculation, we conducted comparisons using 4 real datasets. Interchromosomal LD patterns observed potentially reflect levels of selection intensity across different species. Both versions of GWLD, the R package (https://github.com/Rong-Zh/GWLD/GWLD-R) and the standalone C++ software (https://github.com/Rong-Zh/GWLD/GWLD-C++), are freely available on GitHub.

Keywords: C++; Rcpp package; genome-wide analysis; linkage disequilibrium; mutual information; reduced mutual information.

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Conflict of interest statement

Conflicts of interest The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
GWLD overview. Mainly composed of 3 modules: data processing, computation of LD measures, and visualization.
Fig. 2.
Fig. 2.
Correlation plots between LD measures. a–c) RMI, MI, and r2 heatmaps for duck chromosome 26. d) Genome-wide LD decay for ducks. e) Main diagonal, histogram and fitted curves of LD value distributions. Upper right, correlation between different LD measures. Lower left, scattered plots and fitted lines for different LD measures.
Fig. 3.
Fig. 3.
Genome-wide view of interchromosomal LD patterns. RMI was calculated and used as the LD measure. Pig and maize had more SNP pairs with high interchromosomal LD values. a–d) Duck, human, pig, and maize. Left panel, circos-like plots for interchromosomal LD values. Right panel, histogram of numbers of SNP pairs with different interchromosomal LD values. x-axis, distribution of LD intervals; y-axis, number of SNP pairs.
Fig. 4.
Fig. 4.
Benchmark analyses on computing speeds between GWLD and other software (Tassel, Plink, and R Genetics package). The duck data were used as an example.
Fig. 5.
Fig. 5.
Evaluation of the effects of numbers of SNPs and samples on GWLD computing speeds. The duck data were used as an example. a and b) Time required for calculation (in seconds). c and d) Relative Pearson’s correlation between LD measures.
See this image and copyright information in PMC

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