Big-LD
Sunah Kim (sunny03@snu.ac.kr)
Big-LD
Big-LD is a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. The detailed information about the Big-LD can be found in our paper published in bioinformatics.
Installation
library("devtools")
devtools::install_github("sunnyeesl/BigLD")library(BigLD)Data
You need an additive genotype data (each SNP genotype is coded in terms of the number of minor alleles) and a SNP information data. The package include sample genotype data and SNPinfo data.
Load the sample data (if you installed the BigLD packages).
data(geno)
data(SNPinfo)Or simply you can download the sample data from /inst/extdata The sample data include 1000SNPs and 286 individuals.
geno[1:10, 1:7]## rs174309 rs174310 rs5747216 rs174312 rs174313 rs5747217 rs174314
## 1 0 0 0 0 0 0 0
## 2 1 1 0 0 1 1 1
## 3 0 0 0 0 0 0 0
## 4 1 1 0 0 1 1 1
## 5 1 1 0 0 1 1 1
## 6 2 2 0 0 2 2 2
## 7 2 2 0 1 2 1 2
## 8 0 0 1 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 1 1 1 0 1 1 1head(SNPinfo)## rsID bp
## 1 rs174309 18000090
## 2 rs174310 18000280
## 3 rs5747216 18000829
## 4 rs174312 18001109
## 5 rs174313 18001375
## 6 rs5747217 18001894CLQD
CLQD partitioning the SNPs into subgroups such that each subgroup contains highly correlated SNPs. There are two CLQ methods, original CLQ(ClQmode = 'Maximal') and CLQD (ClQmode = 'Density').
CLQres = CLQD(geno, SNPinfo, CLQmode = 'Density')## [1] "end pre-steps"head(CLQres, n = 20)## [1] 25 25 106 57 25 81 25 25 57 57 15 15 15 81 57 26 26
## [18] 26 81 57Big_LD
’Big_LD` returns the estimation of LD block regions of given data.
BigLDres = Big_LD(geno, SNPinfo)## [1] "split whole sequence into subsegments"
## [1] "cutting sequence, done"
## [1] "there is only one sub-region!"
## [1] "end pre-steps"
## [1] "CLQ done!"
## [1] 1 1
## [1] "2017-10-19 17:00:34 KST"BigLDres## start end start.rsID end.rsID start.bp end.bp
## 1 1 2 rs174309 rs174310 18000090 18000280
## 2 3 54 rs5747216 rs2268780 18000829 18031530
## 3 58 69 rs174346 rs174358 18036253 18043090
## 4 70 77 rs174360 rs174365 18044257 18045084
## 5 78 101 rs174366 rs423158 18046680 18053496
## 6 102 113 rs1296810 rs148048073 18054369 18057141
## 7 114 116 rs75599514 rs77113684 18057200 18057362
## 8 119 120 rs60773453 rs74276474 18057926 18057936
## 9 121 123 rs74196725 rs12484668 18059204 18060356
## 10 124 132 rs185617591 rs4819604 18060385 18060457
## 11 136 161 rs2074343 rs5992751 18065981 18080154
## 12 162 302 rs73391480 rs5747302 18080431 18118204
## 13 303 512 rs9604777 rs1296687 18118636 18231046
## 14 513 538 rs2895951 rs181413 18232368 18239312
## 15 539 540 rs181414 rs181415 18240212 18240260
## 16 543 566 rs415050 rs443912 18242182 18257138
## 17 567 581 rs8190256 rs116984560 18258344 18263834
## 18 583 584 rs5992838 rs1076489 18264831 18265172
## 19 585 587 rs9617618 rs12165723 18265271 18266989
## 20 588 596 rs73380798 rs79268089 18267982 18271491
## 21 599 600 rs382013 rs429357 18276101 18277314
## 22 602 611 rs117306911 rs5992105 18278320 18283247
## 23 612 624 rs7291975 rs389496 18283876 18289204
## 24 625 653 rs8140645 rs399757 18289555 18295575
## 25 654 715 rs1550663 rs5992871 18296238 18310110
## 26 716 717 rs5992872 rs5992121 18310363 18310367
## 27 719 720 rs7287465 rs5992122 18311845 18312343
## 28 721 741 rs8136428 rs453841 18313018 18317821
## 29 742 758 rs415170 rs748779 18318963 18325067
## 30 760 764 rs2587111 rs2587113 18326754 18328503
## 31 765 766 rs2587114 rs2111546 18329146 18329411
## 32 767 772 rs9618143 rs10427597 18329571 18332410
## 33 774 851 rs9617628 rs4819473 18333467 18380081
## 34 852 877 rs56076143 rs5747406 18380917 18395952
## 35 879 882 rs5747408 rs9604802 18397120 18398018
## 36 883 1000 rs9604803 rs9605461 18398207 18459658If you want to apply heuristic procedure, add option checkLargest = TRUE.
Big_LD(geno, SNPinfo, MAFcut = 0.05, checkLargest = TRUE, appendrare = TRUE)LDblockHeatmap
LDblockHeatmap visualize the LDblock boundaries detected by Big_LD.
You can input the results obtained using Big-LD (LDblockResult= BigLDres). If you do not input a Big-LD results, the LDblockHeatmap function first excute Big_LD function to obtain an LD block estimation result.
LDblockHeatmap(geno, SNPinfo, 22, LDblockResult= BigLDres)
You can show the location of the specific SNPs (showSNPs = SNPinfo[c(100, 200), ] shows the 100th and 200th SNPs), or give the threshold for LD block sizes to show SNP information (showLDsize = 50). If you want to save the LD heatmap results as tif file, add options such as savefile = TRUE, filename = "LDheatmap2.tif".
LDblockHeatmap(geno, SNPinfo, 22, showSNPs = SNPinfo[c(100, 200), ], showLDsize = 50, savefile = TRUE, filename = "LDheatmap2.tif")