BLD-LDAK Annotations

The BLD-LDAK and BLD-LDAK+Alpha Models use 65 SNP annotations. Click here to download the first 64 annotations (warning, the file is 800Mb). Note that you will have to create the final annotation (the LDAK weightings) yourself; for more details see Technical Details.

You only need to download these annotations if you plan to calculate the tagging file yourself (they are not necessary if you instead use Pre-Computed Taggings).

Note that the predictor names are in the format Chr:BP:A1:A2, where the alleles A1 and A2 are in alphabetical order. If your predictor names are in a different format, here is a script to change the bimfile (it assumes the data has the prefix data).

mv data.bim data.bim.old
awk < data.bim.old '{$2=$1":"$4":"$5":"$6;if($6<$5){$2=$1":"$4":"$6":"$5}print $0}' > data.bim
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We obtained the 64 annotations as follows:

First we downloaded the folder 1000G_Phase3_baselineLD_v2.1_ldscores.tgz from https://data.broadinstitute.org/alkesgroup/LDSCORE. Within this folder, the .annot.gz files contain the 74 annotations of the Baseline LD Model. The BLD-LDAK Model uses Annotations 1-58 and 59-64.

We extracted all 74 annotations (plus Annotation 0, the base category) using the following commands:
rm bld0 base{1..74}
for j in {1..22}; do gunzip -c baselineLD_v1.1/baselineLD.$j.annot.gz | awk '(NR>1){for(j=1;j<=74;j++){if($(5+j)!=0){print $1":"$2, $(5+j) >> "base"j}}print $1":"$2 >> "bld0"}'; done

Then we excluded the 10 MAF bins using these two commands:
for j in {1..58}; do cp base$j > bld$j; done
for j in {59..64}; do cp base$((10+j)) bld$j; done

Note that in the files we provide, SNPs have been renamed in the format Chr:BP:X:Y, where X and Y are the two alleles in alphabetical order
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The names of the annotations are:

1 Coding_UCSC
2 Coding_UCSC.extend.500
3 Conserved_LindbladToh
4 Conserved_LindbladToh.extend.500
5 CTCF_Hoffman
6 CTCF_Hoffman.extend.500
7 DGF_ENCODE
8 DGF_ENCODE.extend.500
9 DHS_peaks_Trynka
10 DHS_Trynka
11 DHS_Trynka.extend.500
12 Enhancer_Andersson
13 Enhancer_Andersson.extend.500
14 Enhancer_Hoffman
15 Enhancer_Hoffman.extend.500
16 FetalDHS_Trynka
17 FetalDHS_Trynka.extend.500
18 H3K27ac_Hnisz
19 H3K27ac_Hnisz.extend.500
20 H3K27ac_PGC2
21 H3K27ac_PGC2.extend.500
22 H3K4me1_peaks_Trynka
23 H3K4me1_Trynka
24 H3K4me1_Trynka.extend.500
25 H3K4me3_peaks_Trynka
26 H3K4me3_Trynka
27 H3K4me3_Trynka.extend.500
28 H3K9ac_peaks_Trynka
29 H3K9ac_Trynka
30 H3K9ac_Trynka.extend.500
31 Intron_UCSC
32 Intron_UCSC.extend.500
33 PromoterFlanking_Hoffman
34 PromoterFlanking_Hoffman.extend.500
35 Promoter_UCSC
36 Promoter_UCSC.extend.500
37 Repressed_Hoffman
38 Repressed_Hoffman.extend.500
39 SuperEnhancer_Hnisz
40 SuperEnhancer_Hnisz.extend.500
41 TFBS_ENCODE
42 TFBS_ENCODE.extend.500
43 Transcr_Hoffman
44 Transcr_Hoffman.extend.500
45 TSS_Hoffman
46 TSS_Hoffman.extend.500
47 UTR_3_UCSC
48 UTR_3_UCSC.extend.500
49 UTR_5_UCSC
50 UTR_5_UCSC.extend.500
51 WeakEnhancer_Hoffman
52 WeakEnhancer_Hoffman.extend.500
53 Super_Enhancer_Vahedi
54 Super_Enhancer_Vahedi.extend.500
55 Typical_Enhancer_Vahedi
56 Typical_Enhancer_Vahedi.extend.500
57 GERP.NS
58 GERP.RSsup4
59 MAF_Adj_Predicted_Allele_Age
60 MAF_Adj_LLD_AFR
61 Recomb_Rate_10kb
62 Nucleotide_Diversity_10kb
63 Backgrd_Selection_Stat
64 CpG_Content_50kb