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LDxBlocks: Genome-Wide LD Block Detection and Haplotype Analysis

Source: R/LDxBlocks-package.R
LDxBlocks-package.Rd

Fast, scalable linkage disequilibrium (LD) block detection for genome-wide SNP data using a C++/Armadillo computational core and two LD metrics: standard r^2 and kinship-adjusted rV^2. Includes haplotype reconstruction, diversity metrics, prediction feature matrices, and multi-format genotype I/O.

Choosing between r^2 and rV^2

r^2 (method = "r2")

Standard squared Pearson correlation. No kinship matrix required. C++ kernel via RcppArmadillo + OpenMP. Suitable for unstructured populations and large datasets (10 M+ markers).

rV^2 (method = "rV2")

Kinship-whitened squared correlation (Mangin et al. 2012, Heredity 108:285-291). Corrects LD inflation from relatedness. For livestock, inbred lines, or family-based cohorts with < 200 k markers per chromosome.

Pipeline overview

  1. Read genotype data: read_geno – auto-detects CSV, HapMap, VCF, GDS, BED, or plain matrix. For WGS panels where peak RAM is a concern, use read_geno_bigmemory to build a memory-mapped store (requires bigmemory).

  2. Detect LD blocks chromosome-wise: run_Big_LD_all_chr.

  3. Optionally auto-tune parameters: tune_LD_params.

  4. Reconstruct haplotypes: extract_haplotypes.

  5. Decode haplotype strings to nucleotides: decode_haplotype_strings.

  6. Compute diversity metrics: compute_haplotype_diversity.

  7. Build prediction feature matrix: build_haplotype_feature_matrix.

  8. Compute LD decay and chromosome-specific decay distances: compute_ld_decay. Provides the critical r\(^2\) threshold (parametric: 95th percentile of unlinked-marker r\(^2\)) and per-chromosome decay distances for candidate gene windows.

  9. Map GWAS hits to QTL regions (LD-aware windows when ld_decay is supplied): define_qtl_regions.

  10. Genomic prediction (Tong et al. 2024/2025): run_haplotype_prediction – full pipeline from BLUEs to block importance; or step-by-step via compute_haplotype_grm, backsolve_snp_effects, compute_local_gebv, rank_haplotype_blocks.

  11. Haplotype association testing with simpleM correction (Gao et al. 2008, 2010, 2011): test_block_haplotypes. sig_metric selects among p_wald, p_fdr, p_simplem (Bonferroni-style), and p_simplem_sidak (Sidak-style, recommended). All four p-value columns always present. meff_scope controls Meff estimation scope.

  12. Cross-population GWAS validation - two functions depending on how the association analysis was performed:

    • compare_block_effects - for results from test_block_haplotypes. IVW meta-analysis, Cochran Q, I-squared, direction agreement per block.

    • compare_gwas_effects - for results from external GWAS tools (GAPIT, TASSEL, FarmCPU, PLINK, etc.). Accepts raw GWAS data frames or define_qtl_regions output. Derives SE from z-score when absent.

    • Both functions support block_match = "position" to match LD blocks by genomic interval overlap (Intersection-over-Union) rather than block_id string equality. This handles the common case where block boundaries differ between populations due to different ancestral LD structures. overlap_min (default 0.50) sets the minimum IoU for a valid positional match. The match_type output column reports "exact", "position", or "pop1_only" for every block.

  13. Write outputs: write_haplotype_numeric, write_haplotype_character, write_haplotype_diversity.

  14. Or run everything at once: run_ldx_pipeline.

Example data

ldx_geno

120 x 230 genotype matrix, 3 chromosomes, 9 simulated LD blocks.

ldx_snp_info

SNP metadata (SNP, CHR, POS, REF, ALT).

ldx_blocks

Reference block table for ldx_geno.

ldx_gwas

20 toy GWAS markers for tuning demos.

ldx_blues

Pre-adjusted BLUEs (id, YLD, RES) for run_haplotype_prediction demos. Available as an .rda dataset and inst/extdata/example_blues.csv.

References

Kim S-A et al. (2018). A new haplotype block detection method for dense genome sequencing data based on interval graph modeling and dynamic programming. Bioinformatics 34(4):588-596. doi:10.1093/bioinformatics/btx609

Mangin B et al. (2012). Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity 108(3):285-291. doi:10.1038/hdy.2011.73

VanRaden PM (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science 91(11):4414-4423. doi:10.3168/jds.2007-0980

Difabachew YF et al. (2023). Genomic prediction with haplotype blocks in wheat. Frontiers in Plant Science 14:1168547. doi:10.3389/fpls.2023.1168547

Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP (2023). Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. Frontiers in Plant Science 14:1217589. doi:10.3389/fpls.2023.1217589

Pook T et al. (2019). HaploBlocker: Creation of subgroup-specific haplotype blocks and libraries. Genetics 212(4):1045-1061. doi:10.1534/genetics.119.302283

Tong J et al. (2024). Stacking beneficial haplotypes from the Vavilov wheat collection to accelerate breeding for multiple disease resistance. Theoretical and Applied Genetics 137:274. doi:10.1007/s00122-024-04784-w

Tong J et al. (2025). Haplotype stacking to improve stability of stripe rust resistance in wheat. Theoretical and Applied Genetics 138:267. doi:10.1007/s00122-025-05045-0

Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008:P10008. doi:10.1088/1742-5468/2008/10/P10008

Traag VA, Waltman L, van Eck NJ (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9:5233. doi:10.1038/s41598-019-41695-z

Hill WG, Weir BS (1988). Variances and covariances of squared linkage disequilibria in finite populations. Theoretical Population Biology 33(1):54-78. doi:10.1016/0040-5809(88)90004-4

Remington DL et al. (2001). Structure of linkage disequilibrium and phenotypic associations in the maize genome. PNAS 98(20):11479-11484. doi:10.1073/pnas.201394398

Gao X, Starmer J, Martin ER (2008). A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genetic Epidemiology 32:361-369. doi:10.1002/gepi.20310

Gao X, Becker LC, Becker DM, Starmer JD, Province MA (2010). Avoiding the high Bonferroni penalty in genome-wide association studies. Genetic Epidemiology 34:100-105. doi:10.1002/gepi.20430

Gao X (2011). Multiple testing corrections for imputed SNPs. Genetic Epidemiology 35:154-158. doi:10.1002/gepi.20563

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009). Introduction to Meta-Analysis. Wiley.

Higgins JPT, Thompson SG (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21(11):1539-1558. doi:10.1002/sim.1186

See also

Useful links:

Author

Maintainer: Félicien Akohoue akohoue.f@gmail.com (ORCID)